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Xie Z, Weng L, He J, Feng X, Xu X, Ma Y, Bai P, Kong Q. PNNGS, a multi-convolutional parallel neural network for genomic selection. FRONTIERS IN PLANT SCIENCE 2024; 15:1410596. [PMID: 39290743 PMCID: PMC11405342 DOI: 10.3389/fpls.2024.1410596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 08/19/2024] [Indexed: 09/19/2024]
Abstract
Genomic selection (GS) can accomplish breeding faster than phenotypic selection. Improving prediction accuracy is the key to promoting GS. To improve the GS prediction accuracy and stability, we introduce parallel convolution to deep learning for GS and call it a parallel neural network for genomic selection (PNNGS). In PNNGS, information passes through convolutions of different kernel sizes in parallel. The convolutions in each branch are connected with residuals. Four different Lp loss functions train PNNGS. Through experiments, the optimal number of parallel paths for rice, sunflower, wheat, and maize is found to be 4, 6, 4, and 3, respectively. Phenotype prediction is performed on 24 cases through ridge-regression best linear unbiased prediction (RRBLUP), random forests (RF), support vector regression (SVR), deep neural network genomic prediction (DNNGP), and PNNGS. Serial DNNGP and parallel PNNGS outperform the other three algorithms. On average, PNNGS prediction accuracy is 0.031 larger than DNNGP prediction accuracy, indicating that parallelism can improve the GS model. Plants are divided into clusters through principal component analysis (PCA) and K-means clustering algorithms. The sample sizes of different clusters vary greatly, indicating that this is unbalanced data. Through stratified sampling, the prediction stability and accuracy of PNNGS are improved. When the training samples are reduced in small clusters, the prediction accuracy of PNNGS decreases significantly. Increasing the sample size of small clusters is critical to improving the prediction accuracy of GS.
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Affiliation(s)
- Zhengchao Xie
- Research Center for Life Sciences Computing, Zhejiang Laboratory, Hangzhou, China
| | - Lin Weng
- Research Center for Life Sciences Computing, Zhejiang Laboratory, Hangzhou, China
| | - Jingjing He
- Research Center for Life Sciences Computing, Zhejiang Laboratory, Hangzhou, China
| | - Xianzhong Feng
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
| | - Xiaogang Xu
- School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou, China
| | - Yinxing Ma
- Research Center for Life Sciences Computing, Zhejiang Laboratory, Hangzhou, China
| | - Panpan Bai
- Research Center for Life Sciences Computing, Zhejiang Laboratory, Hangzhou, China
| | - Qihui Kong
- Research Center for Life Sciences Computing, Zhejiang Laboratory, Hangzhou, China
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2
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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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Li C, Yang Q, Liu B, Shi X, Liu Z, Yang C, Wang T, Xiao F, Zhang M, Shi A, Yan L. Ability of Genomic Prediction to Bi-Parent-Derived Breeding Population Using Public Data for Soybean Oil and Protein Content. PLANTS (BASEL, SWITZERLAND) 2024; 13:1260. [PMID: 38732474 PMCID: PMC11085238 DOI: 10.3390/plants13091260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 04/21/2024] [Accepted: 04/29/2024] [Indexed: 05/13/2024]
Abstract
Genomic selection (GS) is a marker-based selection method used to improve the genetic gain of quantitative traits in plant breeding. A large number of breeding datasets are available in the soybean database, and the application of these public datasets in GS will improve breeding efficiency and reduce time and cost. However, the most important problem to be solved is how to improve the ability of across-population prediction. The objectives of this study were to perform genomic prediction (GP) and estimate the prediction ability (PA) for seed oil and protein contents in soybean using available public datasets to predict breeding populations in current, ongoing breeding programs. In this study, six public datasets of USDA GRIN soybean germplasm accessions with available phenotypic data of seed oil and protein contents from different experimental populations and their genotypic data of single-nucleotide polymorphisms (SNPs) were used to perform GP and to predict a bi-parent-derived breeding population in our experiment. The average PA was 0.55 and 0.50 for seed oil and protein contents within the bi-parents population according to the within-population prediction; and 0.45 for oil and 0.39 for protein content when the six USDA populations were combined and employed as training sets to predict the bi-parent-derived population. The results showed that four USDA-cultivated populations can be used as a training set individually or combined to predict oil and protein contents in GS when using 800 or more USDA germplasm accessions as a training set. The smaller the genetic distance between training population and testing population, the higher the PA. The PA increased as the population size increased. In across-population prediction, no significant difference was observed in PA for oil and protein content among different models. The PA increased as the SNP number increased until a marker set consisted of 10,000 SNPs. This study provides reasonable suggestions and methods for breeders to utilize public datasets for GS. It will aid breeders in developing GS-assisted breeding strategies to develop elite soybean cultivars with high oil and protein contents.
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Affiliation(s)
- Chenhui Li
- College of Life Sciences, Hebei Agricultural University, Baoding 071001, China;
- Hebei Laboratory of Crop Genetics and Breeding, National Soybean Improvement Center Shijiazhuang Sub-Center, Huang-Huai-Hai Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture and Rural Affairs, Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, High-Tech Industrial Development Zone, 162 Hengshan St., Shijiazhuang 050035, China; (Q.Y.); (B.L.); (X.S.); (Z.L.); (C.Y.)
| | - Qing Yang
- Hebei Laboratory of Crop Genetics and Breeding, National Soybean Improvement Center Shijiazhuang Sub-Center, Huang-Huai-Hai Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture and Rural Affairs, Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, High-Tech Industrial Development Zone, 162 Hengshan St., Shijiazhuang 050035, China; (Q.Y.); (B.L.); (X.S.); (Z.L.); (C.Y.)
| | - Bingqiang Liu
- Hebei Laboratory of Crop Genetics and Breeding, National Soybean Improvement Center Shijiazhuang Sub-Center, Huang-Huai-Hai Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture and Rural Affairs, Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, High-Tech Industrial Development Zone, 162 Hengshan St., Shijiazhuang 050035, China; (Q.Y.); (B.L.); (X.S.); (Z.L.); (C.Y.)
| | - Xiaolei Shi
- Hebei Laboratory of Crop Genetics and Breeding, National Soybean Improvement Center Shijiazhuang Sub-Center, Huang-Huai-Hai Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture and Rural Affairs, Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, High-Tech Industrial Development Zone, 162 Hengshan St., Shijiazhuang 050035, China; (Q.Y.); (B.L.); (X.S.); (Z.L.); (C.Y.)
| | - Zhi Liu
- Hebei Laboratory of Crop Genetics and Breeding, National Soybean Improvement Center Shijiazhuang Sub-Center, Huang-Huai-Hai Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture and Rural Affairs, Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, High-Tech Industrial Development Zone, 162 Hengshan St., Shijiazhuang 050035, China; (Q.Y.); (B.L.); (X.S.); (Z.L.); (C.Y.)
| | - Chunyan Yang
- Hebei Laboratory of Crop Genetics and Breeding, National Soybean Improvement Center Shijiazhuang Sub-Center, Huang-Huai-Hai Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture and Rural Affairs, Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, High-Tech Industrial Development Zone, 162 Hengshan St., Shijiazhuang 050035, China; (Q.Y.); (B.L.); (X.S.); (Z.L.); (C.Y.)
| | - Tao Wang
- Handan Academy of Agricultural Science, Handan 056001, China; (T.W.); (F.X.)
| | - Fuming Xiao
- Handan Academy of Agricultural Science, Handan 056001, China; (T.W.); (F.X.)
| | - Mengchen Zhang
- Hebei Laboratory of Crop Genetics and Breeding, National Soybean Improvement Center Shijiazhuang Sub-Center, Huang-Huai-Hai Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture and Rural Affairs, Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, High-Tech Industrial Development Zone, 162 Hengshan St., Shijiazhuang 050035, China; (Q.Y.); (B.L.); (X.S.); (Z.L.); (C.Y.)
| | - Ainong Shi
- Department of Horticulture, University of Arkansas, Fayetteville, AR 72701, USA
| | - Long Yan
- Hebei Laboratory of Crop Genetics and Breeding, National Soybean Improvement Center Shijiazhuang Sub-Center, Huang-Huai-Hai Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture and Rural Affairs, Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, High-Tech Industrial Development Zone, 162 Hengshan St., Shijiazhuang 050035, China; (Q.Y.); (B.L.); (X.S.); (Z.L.); (C.Y.)
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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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5
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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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6
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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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7
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Rabieyan E, Darvishzadeh R, Alipour H. Genetic analyses and prediction for lodging‑related traits in a diverse Iranian hexaploid wheat collection. Sci Rep 2024; 14:275. [PMID: 38167972 PMCID: PMC10761700 DOI: 10.1038/s41598-023-49927-z] [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: 05/02/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024] Open
Abstract
Lodging is one of the most important limiting environmental factors for achieving the maximum yield and quality of grains in cereals, including wheat. However, little is known about the genetic foundation underlying lodging resistance (LR) in wheat. In this study, 208 landraces and 90 cultivars were phenotyped in two cropping seasons (2018-2019 and 2019-2020) for 19 LR-related traits. A genome-wide association study (GWAS) and genomics prediction were carried out to dissect the genomic regions of LR. The number of significant marker pairs (MPs) was highest for genome B in both landraces (427,017) and cultivars (37,359). The strongest linkage disequilibrium (LD) between marker pairs was found on chromosome 4A (0.318). For stem lodging-related traits, 465, 497, and 478 marker-trait associations (MTAs) and 45 candidate genes were identified in year 1, year 2, and pooled. Gene ontology exhibited genomic region on Chr. 2B, 6B, and 7B control lodging. Most of these genes have key roles in defense response, calcium ion transmembrane transport, carbohydrate metabolic process, nitrogen compound metabolic process, and some genes harbor unknown functions that, all together may respond to lodging as a complex network. The module associated with starch and sucrose biosynthesis was highlighted. Regarding genomic prediction, the GBLUP model performed better than BRR and RRBLUP. This suggests that GBLUP would be a good tool for wheat genome selection. As a result of these findings, it has been possible to identify pivotal QTLs and genes that could be used to improve stem lodging resistance in Triticum aestivum L.
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Affiliation(s)
- Ehsan Rabieyan
- Department of Plant Production and Genetics, Urmia University, Urmia, Iran
| | - Reza Darvishzadeh
- Department of Plant Production and Genetics, Urmia University, Urmia, Iran
| | - Hadi Alipour
- Department of Plant Production and Genetics, Urmia University, Urmia, Iran.
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8
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Ortiz R. Challenges for crop improvement. Emerg Top Life Sci 2023; 7:197-205. [PMID: 37905719 DOI: 10.1042/etls20230106] [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: 09/24/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 11/02/2023]
Abstract
The genetic improvement of crops faces the significant challenge of feeding an ever-increasing population amidst a changing climate, and when governments are adopting a 'more with less' approach to reduce input use. Plant breeding has the potential to contribute to the United Nations Agenda 2030 by addressing various sustainable development goals (SDGs), with its most profound impact expected on SDG2 Zero Hunger. To expedite the time-consuming crossbreeding process, a genomic-led approach for predicting breeding values, targeted mutagenesis through gene editing, high-throughput phenomics for trait evaluation, enviromics for including characterization of the testing environments, machine learning for effective management of large datasets, and speed breeding techniques promoting early flowering and seed production are being incorporated into the plant breeding toolbox. These advancements are poised to enhance genetic gains through selection in the cultigen pools of various crops. Consequently, these knowledge-based breeding methods are pursued for trait introgression, population improvement, and cultivar development. This article uses the potato crop as an example to showcase the progress being made in both genomic-led approaches and gene editing for accelerating the delivery of genetic gains through the utilization of genetically enhanced elite germplasm. It also further underscores that access to technological advances in plant breeding may be influenced by regulations and intellectual property rights.
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Affiliation(s)
- Rodomiro Ortiz
- Department of Plant Breeding (VF), Swedish University of Agricultural Sciences (SLU), Box 190 Sundsvagen 10, SE 23422 Lomma, Sweden
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9
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Wu C, Zhang Y, Ying Z, Li L, Wang J, Yu H, Zhang M, Feng X, Wei X, Xu X. A transformer-based genomic prediction method fused with knowledge-guided module. Brief Bioinform 2023; 25:bbad438. [PMID: 38058185 PMCID: PMC10701102 DOI: 10.1093/bib/bbad438] [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: 09/07/2023] [Revised: 10/15/2023] [Accepted: 11/03/2023] [Indexed: 12/08/2023] Open
Abstract
Genomic prediction (GP) uses single nucleotide polymorphisms (SNPs) to establish associations between markers and phenotypes. Selection of early individuals by genomic estimated breeding value shortens the generation interval and speeds up the breeding process. Recently, methods based on deep learning (DL) have gained great attention in the field of GP. In this study, we explore the application of Transformer-based structures to GP and develop a novel deep-learning model named GPformer. GPformer obtains a global view by gleaning beneficial information from all relevant SNPs regardless of the physical distance between SNPs. Comprehensive experimental results on five different crop datasets show that GPformer outperforms ridge regression-based linear unbiased prediction (RR-BLUP), support vector regression (SVR), light gradient boosting machine (LightGBM) and deep neural network genomic prediction (DNNGP) in terms of mean absolute error, Pearson's correlation coefficient and the proposed metric consistent index. Furthermore, we introduce a knowledge-guided module (KGM) to extract genome-wide association studies-based information, which is fused into GPformer as prior knowledge. KGM is very flexible and can be plugged into any DL network. Ablation studies of KGM on three datasets illustrate the efficiency of KGM adequately. Moreover, GPformer is robust and stable to hyperparameters and can generalize to each phenotype of every dataset, which is suitable for practical application scenarios.
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Affiliation(s)
- Cuiling Wu
- Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China
| | - Yiyi Zhang
- Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China
| | - Zhiwen Ying
- Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China
| | - Ling Li
- Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China
| | - Jun Wang
- Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China
| | - Hui Yu
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China
| | - Mengchen Zhang
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China
| | - Xianzhong Feng
- Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China
| | - Xinghua Wei
- Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou 310006, China
| | - Xiaogang Xu
- School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, China
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10
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Esposito S, Vitale P, Taranto F, Saia S, Pecorella I, D'Agostino N, Rodriguez M, Natoli V, De Vita P. Simultaneous improvement of grain yield and grain protein concentration in durum wheat by using association tests and weighted GBLUP. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:242. [PMID: 37947927 DOI: 10.1007/s00122-023-04487-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 10/16/2023] [Indexed: 11/12/2023]
Abstract
KEY MESSAGE Simultaneous improvement for GY and GPC by using GWAS and GBLUP suggested a significant application in durum wheat breeding. Despite the importance of grain protein concentration (GPC) in determining wheat quality, its negative correlation with grain yield (GY) is still one of the major challenges for breeders. Here, a durum wheat panel of 200 genotypes was evaluated for GY, GPC, and their derived indices (GPD and GYD), under eight different agronomic conditions. The plant material was genotyped with the Illumina 25 k iSelect array, and a genome-wide association study was performed. Two statistical models revealed dozens of marker-trait associations (MTAs), each explaining up to 30%. phenotypic variance. Two markers on chromosomes 2A and 6B were consistently identified by both models and were found to be significantly associated with GY and GPC. MTAs identified for phenological traits co-mapped to well-known genes (i.e., Ppd-1, Vrn-1). The significance values (p-values) that measure the strength of the association of each single nucleotide polymorphism marker with the target traits were used to perform genomic prediction by using a weighted genomic best linear unbiased prediction model. The trained models were ultimately used to predict the agronomic performances of an independent durum wheat panel, confirming the utility of genomic prediction, although environmental conditions and genetic backgrounds may still be a challenge to overcome. The results generated through our study confirmed the utility of GPD and GYD to mitigate the inverse GY and GPC relationship in wheat, provided novel markers for marker-assisted selection and opened new ways to develop cultivars through genomic prediction approaches.
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Affiliation(s)
- Salvatore Esposito
- Research Centre for Cereal and Industrial Crops (CREA-CI), CREA - Council for Agricultural Research and Economics, SS 673 Meters 25200, 71122, Foggia, Italy
| | - Paolo Vitale
- Research Centre for Cereal and Industrial Crops (CREA-CI), CREA - Council for Agricultural Research and Economics, SS 673 Meters 25200, 71122, Foggia, Italy
- Department of Agriculture, Food, Natural Science, Engineering, University of Foggia, Via Napoli 25, 71122, Foggia, Italy
| | - Francesca Taranto
- Institute of Biosciences and Bioresources (CNR-IBBR), Via Amendola 165/A, 70126, Bari, Italy
| | - Sergio Saia
- Department of Veterinary Sciences, University of Pisa, 56129, Pisa, Italy
| | - Ivano Pecorella
- Research Centre for Cereal and Industrial Crops (CREA-CI), CREA - Council for Agricultural Research and Economics, SS 673 Meters 25200, 71122, Foggia, Italy
| | - Nunzio D'Agostino
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy
| | - Monica Rodriguez
- Department of Agriculture, University of Sassari, Viale Italia, 39, 07100, Sassari, Italy
| | - Vincenzo Natoli
- Genetic Services SRL, Contrada Catenaccio, snc, 71026, Deliceto, FG, Italy
| | - Pasquale De Vita
- Research Centre for Cereal and Industrial Crops (CREA-CI), CREA - Council for Agricultural Research and Economics, SS 673 Meters 25200, 71122, Foggia, Italy.
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11
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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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12
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Wu K, Xu C, Li T, Ma H, Gong J, Li X, Sun X, Hu X. Application of Nanotechnology in Plant Genetic Engineering. Int J Mol Sci 2023; 24:14836. [PMID: 37834283 PMCID: PMC10573821 DOI: 10.3390/ijms241914836] [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: 08/29/2023] [Revised: 09/20/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
The ever-increasing food requirement with globally growing population demands advanced agricultural practices to improve grain yield, to gain crop resilience under unpredictable extreme weather, and to reduce production loss caused by insects and pathogens. To fulfill such requests, genome engineering technology has been applied to various plant species. To date, several generations of genome engineering methods have been developed. Among these methods, the new mainstream technology is clustered regularly interspaced short palindromic repeats (CRISPR) with nucleases. One of the most important processes in genome engineering is to deliver gene cassettes into plant cells. Conventionally used systems have several shortcomings, such as being labor- and time-consuming procedures, potential tissue damage, and low transformation efficiency. Taking advantage of nanotechnology, the nanoparticle-mediated gene delivery method presents technical superiority over conventional approaches due to its high efficiency and adaptability in different plant species. In this review, we summarize the evolution of plant biomolecular delivery methods and discussed their characteristics as well as limitations. We focused on the cutting-edge nanotechnology-based delivery system, and reviewed different types of nanoparticles, preparation of nanomaterials, mechanism of nanoparticle transport, and advanced application in plant genome engineering. On the basis of established methods, we concluded that the combination of genome editing, nanoparticle-mediated gene transformation and de novo regeneration technologies can accelerate crop improvement efficiently in the future.
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Affiliation(s)
- Kexin Wu
- Collaborative Innovation Center for Efficient and Green Production of Agriculture in Mountainous Areas of Zhejiang Province, College of Horticulture Science, Zhejiang A&F University, Hangzhou 311300, China
- Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs, Hangzhou 311300, China
| | - Changbin Xu
- Collaborative Innovation Center for Efficient and Green Production of Agriculture in Mountainous Areas of Zhejiang Province, College of Horticulture Science, Zhejiang A&F University, Hangzhou 311300, China
- Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs, Hangzhou 311300, China
| | - Tong Li
- Collaborative Innovation Center for Efficient and Green Production of Agriculture in Mountainous Areas of Zhejiang Province, College of Horticulture Science, Zhejiang A&F University, Hangzhou 311300, China
- Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs, Hangzhou 311300, China
| | - Haijie Ma
- Collaborative Innovation Center for Efficient and Green Production of Agriculture in Mountainous Areas of Zhejiang Province, College of Horticulture Science, Zhejiang A&F University, Hangzhou 311300, China
- Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs, Hangzhou 311300, China
| | - Jinli Gong
- Collaborative Innovation Center for Efficient and Green Production of Agriculture in Mountainous Areas of Zhejiang Province, College of Horticulture Science, Zhejiang A&F University, Hangzhou 311300, China
- Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs, Hangzhou 311300, China
| | - Xiaolong Li
- Collaborative Innovation Center for Efficient and Green Production of Agriculture in Mountainous Areas of Zhejiang Province, College of Horticulture Science, Zhejiang A&F University, Hangzhou 311300, China
- Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs, Hangzhou 311300, China
| | - Xuepeng Sun
- Collaborative Innovation Center for Efficient and Green Production of Agriculture in Mountainous Areas of Zhejiang Province, College of Horticulture Science, Zhejiang A&F University, Hangzhou 311300, China
- Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs, Hangzhou 311300, China
| | - Xiaoli Hu
- Collaborative Innovation Center for Efficient and Green Production of Agriculture in Mountainous Areas of Zhejiang Province, College of Horticulture Science, Zhejiang A&F University, Hangzhou 311300, China
- Key Laboratory of Quality and Safety Control for Subtropical Fruit and Vegetable, Ministry of Agriculture and Rural Affairs, Hangzhou 311300, China
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13
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Weber SE, Frisch M, Snowdon RJ, Voss-Fels KP. Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets. FRONTIERS IN PLANT SCIENCE 2023; 14:1217589. [PMID: 37731980 PMCID: PMC10507710 DOI: 10.3389/fpls.2023.1217589] [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: 05/05/2023] [Accepted: 08/21/2023] [Indexed: 09/22/2023]
Abstract
In modern plant breeding, genomic selection is becoming the gold standard for selection of superior genotypes. The basis for genomic prediction models is a set of phenotyped lines along with their genotypic profile. With high marker density and linkage disequilibrium (LD) between markers, genotype data in breeding populations tends to exhibit considerable redundancy. Therefore, interest is growing in the use of haplotype blocks to overcome redundancy by summarizing co-inherited features. Moreover, haplotype blocks can help to capture local epistasis caused by interacting loci. Here, we compared genomic prediction methods that either used single SNPs or haplotype blocks with regards to their prediction accuracy for important traits in crop datasets. We used four published datasets from canola, maize, wheat and soybean. Different approaches to construct haplotype blocks were compared, including blocks based on LD, physical distance, number of adjacent markers and the algorithms implemented in the software "Haploview" and "HaploBlocker". The tested prediction methods included Genomic Best Linear Unbiased Prediction (GBLUP), Extended GBLUP to account for additive by additive epistasis (EGBLUP), Bayesian LASSO and Reproducing Kernel Hilbert Space (RKHS) regression. We found improved prediction accuracy in some traits when using haplotype blocks compared to SNP-based predictions, however the magnitude of improvement was very trait- and model-specific. Especially in settings with low marker density, haplotype blocks can improve genomic prediction accuracy. In most cases, physically large haplotype blocks yielded a strong decrease in prediction accuracy. Especially when prediction accuracy varies greatly across different prediction models, prediction based on haplotype blocks can improve prediction accuracy of underperforming models. However, there is no "best" method to build haplotype blocks, since prediction accuracy varied considerably across methods and traits. Hence, criteria used to define haplotype blocks should not be viewed as fixed biological parameters, but rather as hyperparameters that need to be adjusted for every dataset.
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Affiliation(s)
- Sven E. Weber
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Matthias Frisch
- Department of Biometry and Population Genetics, Justus Liebig University, Giessen, Germany
| | - Rod J. Snowdon
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Kai P. Voss-Fels
- Institute for Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
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14
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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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15
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Jones D, Fornarelli R, Derbyshire M, Gibberd M, Barker K, Hane J. The pursuit of genetic gain in agricultural crops through the application of machine-learning to genomic prediction. Front Genet 2023; 14:1186782. [PMID: 37614817 PMCID: PMC10443705 DOI: 10.3389/fgene.2023.1186782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/24/2023] [Indexed: 08/25/2023] Open
Abstract
Current practice in agriculture applies genomic prediction to assist crop breeding in the analysis of genetic marker data. Genomic selection methods typically use linear mixed models, but using machine-learning may provide further potential for improved selection accuracy, or may provide additional information. Here we describe SelectML, an automated pipeline for testing and comparing the performance of a range of linear mixed model and machine-learning-based genomic selection methods. We demonstrate the use of SelectML on an in silico-generated marker dataset which simulated a randomly-sampled (mixed) and an unevenly-sampled (unbalanced) population, comparing the relative performance of various methods included in SelectML on the two datasets. Although machine-learning based methods performed similarly overall to linear mixed models, they performed worse on the mixed dataset and marginally better on the unbalanced dataset, being more affected than linear mixed models by the imposed sampling bias. SelectML can assist in the training, comparison, and selection of genomic selection models, and is available from https://github.com/darcyabjones/selectml.
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Affiliation(s)
- Darcy Jones
- Centre for Crop and Disease Management, Curtin University, Perth, WA, Australia
| | - Roberta Fornarelli
- Centre for Crop and Disease Management, Curtin University, Perth, WA, Australia
- Curtin Institute for Computation, Curtin University, Perth, WA, Australia
| | - Mark Derbyshire
- Centre for Crop and Disease Management, Curtin University, Perth, WA, Australia
| | - Mark Gibberd
- Centre for Crop and Disease Management, Curtin University, Perth, WA, Australia
| | - Kathryn Barker
- Curtin Institute for Computation, Curtin University, Perth, WA, Australia
| | - James Hane
- Centre for Crop and Disease Management, Curtin University, Perth, WA, Australia
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16
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Taranto F, Esposito S, De Vita P. Genomics for Yield and Yield Components in Durum Wheat. PLANTS (BASEL, SWITZERLAND) 2023; 12:2571. [PMID: 37447132 DOI: 10.3390/plants12132571] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/29/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023]
Abstract
In recent years, many efforts have been conducted to dissect the genetic basis of yield and yield components in durum wheat thanks to linkage mapping and genome-wide association studies. In this review, starting from the analysis of the genetic bases that regulate the expression of yield for developing new durum wheat varieties, we have highlighted how, currently, the reductionist approach, i.e., dissecting the yield into its individual components, does not seem capable of ensuring significant yield increases due to diminishing resources, land loss, and ongoing climate change. However, despite the identification of genes and/or chromosomal regions, controlling the grain yield in durum wheat is still a challenge, mainly due to the polyploidy level of this species. In the review, we underline that the next-generation sequencing (NGS) technologies coupled with improved wheat genome assembly and high-throughput genotyping platforms, as well as genome editing technology, will revolutionize plant breeding by providing a great opportunity to capture genetic variation that can be used in breeding programs. To date, genomic selection provides a valuable tool for modeling optimal allelic combinations across the whole genome that maximize the phenotypic potential of an individual under a given environment.
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Affiliation(s)
- Francesca Taranto
- Institute of Biosciences and Bioresources (CNR-IBBR), 70126 Bari, Italy
| | - Salvatore Esposito
- Research Centre for Cereal and Industrial Crops (CREA-CI), CREA-Council for Agricultural Research and Economics, 71122 Foggia, Italy
| | - Pasquale De Vita
- Research Centre for Cereal and Industrial Crops (CREA-CI), CREA-Council for Agricultural Research and Economics, 71122 Foggia, Italy
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17
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Montesinos-López A, Rivera C, Pinto F, Piñera F, Gonzalez D, Reynolds M, Pérez-Rodríguez P, Li H, Montesinos-López OA, Crossa J. Multimodal deep learning methods enhance genomic prediction of wheat breeding. G3 (BETHESDA, MD.) 2023; 13:jkad045. [PMID: 36869747 PMCID: PMC10151399 DOI: 10.1093/g3journal/jkad045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/05/2023]
Abstract
While several statistical machine learning methods have been developed and studied for assessing the genomic prediction (GP) accuracy of unobserved phenotypes in plant breeding research, few methods have linked genomics and phenomics (imaging). Deep learning (DL) neural networks have been developed to increase the GP accuracy of unobserved phenotypes while simultaneously accounting for the complexity of genotype-environment interaction (GE); however, unlike conventional GP models, DL has not been investigated for when genomics is linked with phenomics. In this study we used 2 wheat data sets (DS1 and DS2) to compare a novel DL method with conventional GP models. Models fitted for DS1 were GBLUP, gradient boosting machine (GBM), support vector regression (SVR) and the DL method. Results indicated that for 1 year, DL provided better GP accuracy than results obtained by the other models. However, GP accuracy obtained for other years indicated that the GBLUP model was slightly superior to the DL. DS2 is comprised only of genomic data from wheat lines tested for 3 years, 2 environments (drought and irrigated) and 2-4 traits. DS2 results showed that when predicting the irrigated environment with the drought environment, DL had higher accuracy than the GBLUP model in all analyzed traits and years. When predicting drought environment with information on the irrigated environment, the DL model and GBLUP model had similar accuracy. The DL method used in this study is novel and presents a strong degree of generalization as several modules can potentially be incorporated and concatenated to produce an output for a multi-input data structure.
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Affiliation(s)
- Abelardo Montesinos-López
- Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, 44430, Guadalajara, Jalisco, Mexico
| | - Carolina Rivera
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Edo. de México, Mexico
| | - Francisco Pinto
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Edo. de México, Mexico
| | - Francisco Piñera
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Edo. de México, Mexico
| | - David Gonzalez
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Edo. de México, Mexico
| | - Mathew Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Edo. de México, Mexico
| | | | - Huihui Li
- Institute of Crop Sciences, The National Key Facility for Crop Gene Resources and Genetic Improvement and CIMMYT China office, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | | | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Edo. de México, Mexico
- Colegio de Postgraduados, Montecillos, Edo. de México, CP 56230, Mexico
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18
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Dreisigacker S, Pérez-Rodríguez P, Crespo-Herrera L, Bentley AR, Crossa J. Results from rapid-cycle recurrent genomic selection in spring bread wheat. G3 (BETHESDA, MD.) 2023; 13:jkad025. [PMID: 36702618 PMCID: PMC10085763 DOI: 10.1093/g3journal/jkad025] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 01/18/2023] [Accepted: 01/19/2023] [Indexed: 01/28/2023]
Abstract
Genomic selection (GS) in wheat breeding programs is of great interest for predicting the genotypic values of individuals, where both additive and nonadditive effects determine the final breeding value of lines. While several simulation studies have shown the efficiency of rapid-cycling GS strategies for parental selection or population improvement, their practical implementations are still lacking in wheat and other crops. In this study, we demonstrate the potential of rapid-cycle recurrent GS (RCRGS) to increase genetic gain for grain yield (GY) in wheat. Our results showed a consistent realized genetic gain for GY after 3 cycles of recombination (C1, C2, and C3) of bi-parental F1s, when summarized across 2 years of phenotyping. For both evaluation years combined, genetic gain through RCRGS reached 12.3% from cycle C0 to C3 and realized gain was 0.28 ton ha-1 per cycle with a GY from C0 (6.88 ton ha-1) to C3 (7.73 ton ha-1). RCRGS was also associated with some changes in important agronomic traits that were measured (days to heading, days to maturity, and plant height) but not selected for. To account for these changes, we recommend implementing GS together with multi-trait prediction models.
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Affiliation(s)
- Susanne Dreisigacker
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, Texcoco, Edo. de México, CP 56100, México
| | | | - Leonardo Crespo-Herrera
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, Texcoco, Edo. de México, CP 56100, México
| | - Alison R Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, Texcoco, Edo. de México, CP 56100, México
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera México-Veracruz, Texcoco, Edo. de México, CP 56100, México
- Colegio de Postgraduados, Montecillos, Edo. de México, CP 56264, México
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Gesesse CA, Nigir B, de Sousa K, Gianfranceschi L, Gallo GR, Poland J, Kidane YG, Abate Desta E, Fadda C, Pè ME, Dell’Acqua M. Genomics-driven breeding for local adaptation of durum wheat is enhanced by farmers' traditional knowledge. Proc Natl Acad Sci U S A 2023; 120:e2205774119. [PMID: 36972461 PMCID: PMC10083613 DOI: 10.1073/pnas.2205774119] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 11/14/2022] [Indexed: 03/29/2023] Open
Abstract
In the smallholder, low-input farming systems widespread in sub-Saharan Africa, farmers select and propagate crop varieties based on their traditional knowledge and experience. A data-driven integration of their knowledge into breeding pipelines may support the sustainable intensification of local farming. Here, we combine genomics with participatory research to tap into traditional knowledge in smallholder farming systems, using durum wheat (Triticum durum Desf.) in Ethiopia as a case study. We developed and genotyped a large multiparental population, called the Ethiopian NAM (EtNAM), that recombines an elite international breeding line with Ethiopian traditional varieties maintained by local farmers. A total of 1,200 EtNAM lines were evaluated for agronomic performance and farmers' appreciation in three locations in Ethiopia, finding that women and men farmers could skillfully identify the worth of wheat genotypes and their potential for local adaptation. We then trained a genomic selection (GS) model using farmer appreciation scores and found that its prediction accuracy over grain yield (GY) was higher than that of a benchmark GS model trained on GY. Finally, we used forward genetics approaches to identify marker-trait associations for agronomic traits and farmer appreciation scores. We produced genetic maps for individual EtNAM families and used them to support the characterization of genomic loci of breeding relevance with pleiotropic effects on phenology, yield, and farmer preference. Our data show that farmers' traditional knowledge can be integrated in genomics-driven breeding to support the selection of best allelic combinations for local adaptation.
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Affiliation(s)
- Cherinet Alem Gesesse
- Center of Plant Sciences, Scuola Superiore Sant’Anna, Pisa56127, Italy
- Amhara Regional Agricultural Research Institute, Bahir Dar6000, Ethiopia
| | - Bogale Nigir
- Center of Plant Sciences, Scuola Superiore Sant’Anna, Pisa56127, Italy
| | - Kauê de Sousa
- Digital Inclusion, Bioversity International, Parc Scientifique Agropolis II, Montpellier34397, France
- Department of Agricultural Sciences, Inland Norway University of Applied Sciences, Hamar2322, Norway
| | | | | | - Jesse Poland
- Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal23955-6900, Saudi Arabia
| | - Yosef Gebrehawaryat Kidane
- Center of Plant Sciences, Scuola Superiore Sant’Anna, Pisa56127, Italy
- Biodiversity for Food and Agriculture, Bioversity International, Addis Ababa1000, Ethiopia; and
| | - Ermias Abate Desta
- Amhara Regional Agricultural Research Institute, Bahir Dar6000, Ethiopia
| | - Carlo Fadda
- Biodiversity for Food and Agriculture, Bioversity International, Nairobi00621, Kenya
| | - Mario Enrico Pè
- Center of Plant Sciences, Scuola Superiore Sant’Anna, Pisa56127, Italy
| | - Matteo Dell’Acqua
- Center of Plant Sciences, Scuola Superiore Sant’Anna, Pisa56127, Italy
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20
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Broccanello C, Bellin D, DalCorso G, Furini A, Taranto F. Genetic approaches to exploit landraces for improvement of Triticum turgidum ssp. durum in the age of climate change. FRONTIERS IN PLANT SCIENCE 2023; 14:1101271. [PMID: 36778704 PMCID: PMC9911883 DOI: 10.3389/fpls.2023.1101271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 01/09/2023] [Indexed: 06/18/2023]
Abstract
Addressing the challenges of climate change and durum wheat production is becoming an important driver for food and nutrition security in the Mediterranean area, where are located the major producing countries (Italy, Spain, France, Greece, Morocco, Algeria, Tunisia, Turkey, and Syria). One of the emergent strategies, to cope with durum wheat adaptation, is the exploration and exploitation of the existing genetic variability in landrace populations. In this context, this review aims to highlight the important role of durum wheat landraces as a useful genetic resource to improve the sustainability of Mediterranean agroecosystems, with a focus on adaptation to environmental stresses. We described the most recent molecular techniques and statistical approaches suitable for the identification of beneficial genes/alleles related to the most important traits in landraces and the development of molecular markers for marker-assisted selection. Finally, we outline the state of the art about landraces genetic diversity and signature of selection, already identified from these accessions, for adaptability to the environment.
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Affiliation(s)
| | - Diana Bellin
- Department of Biotechnology, University of Verona, Verona, Italy
| | | | - Antonella Furini
- Department of Biotechnology, University of Verona, Verona, Italy
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21
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Jubair S, Domaratzki M. Crop genomic selection with deep learning and environmental data: A survey. Front Artif Intell 2023; 5:1040295. [PMID: 36703955 PMCID: PMC9871498 DOI: 10.3389/frai.2022.1040295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/22/2022] [Indexed: 01/12/2023] Open
Abstract
Machine learning techniques for crop genomic selections, especially for single-environment plants, are well-developed. These machine learning models, which use dense genome-wide markers to predict phenotype, routinely perform well on single-environment datasets, especially for complex traits affected by multiple markers. On the other hand, machine learning models for predicting crop phenotype, especially deep learning models, using datasets that span different environmental conditions, have only recently emerged. Models that can accept heterogeneous data sources, such as temperature, soil conditions and precipitation, are natural choices for modeling GxE in multi-environment prediction. Here, we review emerging deep learning techniques that incorporate environmental data directly into genomic selection models.
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Affiliation(s)
- Sheikh Jubair
- Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada,*Correspondence: Sheikh Jubair ✉
| | - Mike Domaratzki
- Department of Computer Science, University of Western Ontario, London, ON, Canada
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22
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Rabieyan E, Bihamta MR, Moghaddam ME, Mohammadi V, Alipour H. Genome-wide association mapping and genomic prediction of agronomical traits and breeding values in Iranian wheat under rain-fed and well-watered conditions. BMC Genomics 2022; 23:831. [PMID: 36522726 PMCID: PMC9753272 DOI: 10.1186/s12864-022-08968-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 10/26/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The markers detected by genome-wide association study (GWAS) make it possible to dissect genetic structure and diversity at many loci. This can enable a wheat breeder to reveal and used genomic loci controlling drought tolerance. This study was focused on determining the population structure of Iranian 208 wheat landraces and 90 cultivars via genotyping-by-sequencing (GBS) and also on detecting marker-trait associations (MTAs) by GWAS and genomic prediction (GS) of wheat agronomic traits for drought-tolerance breeding. GWASs were conducted using both the original phenotypes (pGWAS) and estimated breeding values (eGWAS). The bayesian ridge regression (BRR), genomic best linear unbiased prediction (gBLUP), and ridge regression-best linear unbiased prediction (rrBLUP) approaches were used to estimate breeding values and estimate prediction accuracies in genomic selection. RESULTS Population structure analysis using 2,174,975 SNPs revealed four genetically distinct sub-populations from wheat accessions. D-Genome harbored the lowest number of significant marker pairs and the highest linkage disequilibrium (LD), reflecting different evolutionary histories of wheat genomes. From pGWAS, BRR, gBLUP, and rrBLUP, 284, 363, 359 and 295 significant MTAs were found under normal and 195, 365, 362 and 302 under stress conditions, respectively. The gBLUP with the most similarity (80.98 and 71.28% in well-watered and rain-fed environments, correspondingly) with the pGWAS method in the terms of discovered significant SNPs, suggesting the potential of gBLUP in uncovering SNPs. Results from gene ontology revealed that 29 and 30 SNPs in the imputed dataset were located in protein-coding regions for well-watered and rain-fed conditions, respectively. gBLUP model revealed genetic effects better than other models, suggesting a suitable tool for genome selection in wheat. CONCLUSION We illustrate that Iranian landraces of bread wheat contain novel alleles that are adaptive to drought stress environments. gBLUP model can be helpful for fine mapping and cloning of the relevant QTLs and genes, and for carrying out trait introgression and marker-assisted selection in both normal and drought environments in wheat collections.
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Affiliation(s)
- Ehsan Rabieyan
- grid.46072.370000 0004 0612 7950Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences and Engineering, University of Tehran, Karaj, Iran
| | - Mohammad Reza Bihamta
- grid.46072.370000 0004 0612 7950Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences and Engineering, University of Tehran, Karaj, Iran
| | | | - Valiollah Mohammadi
- grid.46072.370000 0004 0612 7950Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences and Engineering, University of Tehran, Karaj, Iran
| | - Hadi Alipour
- grid.412763.50000 0004 0442 8645Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran
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23
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Budhlakoti N, Mishra DC, Majumdar SG, Kumar A, Srivastava S, Rai SN, Rai A. Integrated model for genomic prediction under additive and non-additive genetic architecture. FRONTIERS IN PLANT SCIENCE 2022; 13:1027558. [PMID: 36531414 PMCID: PMC9749549 DOI: 10.3389/fpls.2022.1027558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 10/11/2022] [Indexed: 06/17/2023]
Abstract
Using data from genome-wide molecular markers, genomic selection procedures have proved useful for estimating breeding values and phenotypic prediction. The link between an individual genotype and phenotype has been modelled using a number of parametric methods to estimate individual breeding value. It has been observed that parametric methods perform satisfactorily only when the system under study has additive genetic architecture. To capture non-additive (dominance and epistasis) effects, nonparametric approaches have also been developed; however, they typically fall short of capturing additive effects. The idea behind this study is to select the most appropriate model from each parametric and nonparametric category and build an integrated model that can incorporate the best features of both models. It was observed from the results of the current study that GBLUP performed admirably under additive architecture, while SVM's performance in non-additive architecture was found to be encouraging. A robust model for genomic prediction has been developed in light of these findings, which can handle both additive and epistatic effects simultaneously by minimizing their error variance. The developed integrated model has been assessed using standard evaluation measures like predictive ability and error variance.
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Affiliation(s)
- Neeraj Budhlakoti
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Dwijesh Chandra Mishra
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sayanti Guha Majumdar
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anuj Kumar
- Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada
| | - Sudhir Srivastava
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - S. N. Rai
- Bioinformatics and Biostatistics Department, University of Louisville, Louisville, KY, United States
| | - Anil Rai
- Division of Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
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24
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A divide-and-conquer approach for genomic prediction in rubber tree using machine learning. Sci Rep 2022; 12:18023. [PMID: 36289298 PMCID: PMC9605989 DOI: 10.1038/s41598-022-20416-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 09/13/2022] [Indexed: 01/20/2023] Open
Abstract
Rubber tree (Hevea brasiliensis) is the main feedstock for commercial rubber; however, its long vegetative cycle has hindered the development of more productive varieties via breeding programs. With the availability of H. brasiliensis genomic data, several linkage maps with associated quantitative trait loci have been constructed and suggested as a tool for marker-assisted selection. Nonetheless, novel genomic strategies are still needed, and genomic selection (GS) may facilitate rubber tree breeding programs aimed at reducing the required cycles for performance assessment. Even though such a methodology has already been shown to be a promising tool for rubber tree breeding, increased model predictive capabilities and practical application are still needed. Here, we developed a novel machine learning-based approach for predicting rubber tree stem circumference based on molecular markers. Through a divide-and-conquer strategy, we propose a neural network prediction system with two stages: (1) subpopulation prediction and (2) phenotype estimation. This approach yielded higher accuracies than traditional statistical models in a single-environment scenario. By delivering large accuracy improvements, our methodology represents a powerful tool for use in Hevea GS strategies. Therefore, the incorporation of machine learning techniques into rubber tree GS represents an opportunity to build more robust models and optimize Hevea breeding programs.
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25
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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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26
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Hussain B, Akpınar BA, Alaux M, Algharib AM, Sehgal D, Ali Z, Aradottir GI, Batley J, Bellec A, Bentley AR, Cagirici HB, Cattivelli L, Choulet F, Cockram J, Desiderio F, Devaux P, Dogramaci M, Dorado G, Dreisigacker S, Edwards D, El-Hassouni K, Eversole K, Fahima T, Figueroa M, Gálvez S, Gill KS, Govta L, Gul A, Hensel G, Hernandez P, Crespo-Herrera LA, Ibrahim A, Kilian B, Korzun V, Krugman T, Li Y, Liu S, Mahmoud AF, Morgounov A, Muslu T, Naseer F, Ordon F, Paux E, Perovic D, Reddy GVP, Reif JC, Reynolds M, Roychowdhury R, Rudd J, Sen TZ, Sukumaran S, Ozdemir BS, Tiwari VK, Ullah N, Unver T, Yazar S, Appels R, Budak H. Capturing Wheat Phenotypes at the Genome Level. FRONTIERS IN PLANT SCIENCE 2022; 13:851079. [PMID: 35860541 PMCID: PMC9289626 DOI: 10.3389/fpls.2022.851079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world's most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public-private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.
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Affiliation(s)
- Babar Hussain
- Department of Biological Sciences, Middle East Technical University, Ankara, Turkey
- Department of Biotechnology, Faculty of Life Sciences, University of Central Punjab, Lahore, Pakistan
| | | | - Michael Alaux
- Université Paris-Saclay, INRAE, URGI, Versailles, France
| | - Ahmed M. Algharib
- Department of Environment and Bio-Agriculture, Faculty of Agriculture, Al-Azhar University, Cairo, Egypt
| | - Deepmala Sehgal
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Zulfiqar Ali
- Institute of Plant Breeding and Biotechnology, MNS University of Agriculture, Multan, Pakistan
| | - Gudbjorg I. Aradottir
- Department of Pathology, The National Institute of Agricultural Botany, Cambridge, United Kingdom
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Arnaud Bellec
- French Plant Genomic Resource Center, INRAE-CNRGV, Castanet Tolosan, France
| | - Alison R. Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Halise B. Cagirici
- Crop Improvement and Genetics Research, USDA, Agricultural Research Service, Albany, CA, United States
| | - Luigi Cattivelli
- Council for Agricultural Research and Economics-Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Fred Choulet
- French National Research Institute for Agriculture, Food and the Environment, INRAE, GDEC, Clermont-Ferrand, France
| | - James Cockram
- The John Bingham Laboratory, The National Institute of Agricultural Botany, Cambridge, United Kingdom
| | - Francesca Desiderio
- Council for Agricultural Research and Economics-Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Pierre Devaux
- Research & Innovation, Florimond Desprez Group, Cappelle-en-Pévèle, France
| | - Munevver Dogramaci
- USDA, Agricultural Research Service, Edward T. Schafer Agricultural Research Center, Fargo, ND, United States
| | - Gabriel Dorado
- Department of Bioquímica y Biología Molecular, Campus Rabanales C6-1-E17, Campus de Excelencia Internacional Agroalimentario (ceiA3), Universidad de Córdoba, Córdoba, Spain
| | | | - David Edwards
- University of Western Australia, Perth, WA, Australia
| | - Khaoula El-Hassouni
- State Plant Breeding Institute, The University of Hohenheim, Stuttgart, Germany
| | - Kellye Eversole
- International Wheat Genome Sequencing Consortium (IWGSC), Bethesda, MD, United States
| | - Tzion Fahima
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Melania Figueroa
- Commonwealth Scientific and Industrial Research Organization, Agriculture and Food, Canberra, ACT, Australia
| | - Sergio Gálvez
- Department of Languages and Computer Science, ETSI Informática, Campus de Teatinos, Universidad de Málaga, Andalucía Tech, Málaga, Spain
| | - Kulvinder S. Gill
- Department of Crop Science, Washington State University, Pullman, WA, United States
| | - Liubov Govta
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Alvina Gul
- Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad, Pakistan
| | - Goetz Hensel
- Center of Plant Genome Engineering, Heinrich-Heine-Universität, Düsseldorf, Germany
- Division of Molecular Biology, Centre of Region Haná for Biotechnological and Agriculture Research, Czech Advanced Technology and Research Institute, Palacký University, Olomouc, Czechia
| | - Pilar Hernandez
- Institute for Sustainable Agriculture (IAS-CSIC), Consejo Superior de Investigaciones Científicas (CSIC), Córdoba, Spain
| | | | - Amir Ibrahim
- Crop and Soil Science, Texas A&M University, College Station, TX, United States
| | | | | | - Tamar Krugman
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Yinghui Li
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Shuyu Liu
- Crop and Soil Science, Texas A&M University, College Station, TX, United States
| | - Amer F. Mahmoud
- Department of Plant Pathology, Faculty of Agriculture, Assiut University, Assiut, Egypt
| | - Alexey Morgounov
- Food and Agriculture Organization of the United Nations, Riyadh, Saudi Arabia
| | - Tugdem Muslu
- Molecular Biology, Genetics and Bioengineering, Sabanci University, Istanbul, Turkey
| | - Faiza Naseer
- Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad, Pakistan
| | - Frank Ordon
- Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute, Quedlinburg, Germany
| | - Etienne Paux
- French National Research Institute for Agriculture, Food and the Environment, INRAE, GDEC, Clermont-Ferrand, France
| | - Dragan Perovic
- Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute, Quedlinburg, Germany
| | - Gadi V. P. Reddy
- USDA-Agricultural Research Service, Southern Insect Management Research Unit, Stoneville, MS, United States
| | - Jochen Christoph Reif
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Matthew Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Rajib Roychowdhury
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Jackie Rudd
- Crop and Soil Science, Texas A&M University, College Station, TX, United States
| | - Taner Z. Sen
- Crop Improvement and Genetics Research, USDA, Agricultural Research Service, Albany, CA, United States
| | | | | | | | - Naimat Ullah
- Institute of Biological Sciences (IBS), Gomal University, D. I. Khan, Pakistan
| | - Turgay Unver
- Ficus Biotechnology, Ostim Teknopark, Ankara, Turkey
| | - Selami Yazar
- General Directorate of Research, Ministry of Agriculture, Ankara, Turkey
| | | | - Hikmet Budak
- Montana BioAgriculture, Inc., Missoula, MT, United States
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27
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Kumar S, Jacob SR, Mir RR, Vikas VK, Kulwal P, Chandra T, Kaur S, Kumar U, Kumar S, Sharma S, Singh R, Prasad S, Singh AM, Singh AK, Kumari J, Saharan MS, Bhardwaj SC, Prasad M, Kalia S, Singh K. Indian Wheat Genomics Initiative for Harnessing the Potential of Wheat Germplasm Resources for Breeding Disease-Resistant, Nutrient-Dense, and Climate-Resilient Cultivars. Front Genet 2022; 13:834366. [PMID: 35846116 PMCID: PMC9277310 DOI: 10.3389/fgene.2022.834366] [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: 12/13/2021] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Abstract
Wheat is one of the major staple cereal food crops in India. However, most of the wheat-growing areas experience several biotic and abiotic stresses, resulting in poor quality grains and reduced yield. To ensure food security for the growing population in India, there is a compelling need to explore the untapped genetic diversity available in gene banks for the development of stress-resistant/tolerant cultivars. The improvement of any crop lies in exploring and harnessing the genetic diversity available in its genetic resources in the form of cultivated varieties, landraces, wild relatives, and related genera. A huge collection of wheat genetic resources is conserved in various gene banks across the globe. Molecular and phenotypic characterization followed by documentation of conserved genetic resources is a prerequisite for germplasm utilization in crop improvement. The National Genebank of India has an extensive and diverse collection of wheat germplasm, comprising Indian wheat landraces, primitive cultivars, breeding lines, and collection from other countries. The conserved germplasm can contribute immensely to the development of wheat cultivars with high levels of biotic and abiotic stress tolerance. Breeding wheat varieties that can give high yields under different stress environments has not made much headway due to high genotypes and environmental interaction, non-availability of truly resistant/tolerant germplasm, and non-availability of reliable markers linked with the QTL having a significant impact on resistance/tolerance. The development of new breeding technologies like genomic selection (GS), which takes into account the G × E interaction, will facilitate crop improvement through enhanced climate resilience, by combining biotic and abiotic stress resistance/tolerance and maximizing yield potential. In this review article, we have summarized different constraints being faced by Indian wheat-breeding programs, challenges in addressing biotic and abiotic stresses, and improving quality and nutrition. Efforts have been made to highlight the wealth of Indian wheat genetic resources available in our National Genebank and their evaluation for the identification of trait-specific germplasm. Promising genotypes to develop varieties of important targeted traits and the development of different genomics resources have also been highlighted.
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Affiliation(s)
- Sundeep Kumar
- Indian Council of Agricultural Research-National Bureau of Plant Genetic Resources, New Delhi, India
- *Correspondence: Sundeep Kumar,
| | - Sherry R. Jacob
- Indian Council of Agricultural Research-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Reyazul Rouf Mir
- Division of Genetics and Plant Breeding, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-Kashmir), Jammu and Kashmir, India
| | - V. K. Vikas
- ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Pawan Kulwal
- State Level Biotechnology Centre, Mahatma Phule Krishi Vidyapeeth, Rahuri, India
| | - Tilak Chandra
- Indian Council of Agricultural Research-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Satinder Kaur
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana, India
| | - Uttam Kumar
- Borlaug Institute for South Asia, Ludhiana, India
| | - Suneel Kumar
- Indian Council of Agricultural Research-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Shailendra Sharma
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, Uttar Pradesh
| | - Ravinder Singh
- School of Biotechnology, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu (SKUAST-Jammu), Jammu and Kashmir, India
| | - Sai Prasad
- Indian Agriculture Research Institute Regional Research Station, Indore, India
| | - Anju Mahendru Singh
- Division of Genetics, Indian Agricultural Research Institute, New Delhi, India
| | - Amit Kumar Singh
- Indian Council of Agricultural Research-National Bureau of Plant Genetic Resources, New Delhi, India
| | - Jyoti Kumari
- Indian Council of Agricultural Research-National Bureau of Plant Genetic Resources, New Delhi, India
| | - M. S. Saharan
- Division of Plant Pathology, Indian Agricultural Research Institute, New Delhi, India
| | | | - Manoj Prasad
- Laboratory of Plant Virology, National Institute of Plant Genome Research, New Delhi, India
| | - Sanjay Kalia
- Department of Biotechnology, Ministry of Science and Technology, New Delhi, India
| | - Kuldeep Singh
- Indian Council of Agricultural Research-National Bureau of Plant Genetic Resources, New Delhi, India
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Single trait versus principal component based association analysis for flowering related traits in pigeonpea. Sci Rep 2022; 12:10453. [PMID: 35729192 PMCID: PMC9211048 DOI: 10.1038/s41598-022-14568-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 03/18/2022] [Indexed: 11/08/2022] Open
Abstract
Pigeonpea, a tropical photosensitive crop, harbors significant diversity for days to flowering, but little is known about the genes that govern these differences. Our goal in the current study was to use genome wide association strategy to discover the loci that regulate days to flowering in pigeonpea. A single trait as well as a principal component based association study was conducted on a diverse collection of 142 pigeonpea lines for days to first and fifty percent of flowering over 3 years, besides plant height and number of seeds per pod. The analysis used seven association mapping models (GLM, MLM, MLMM, CMLM, EMLM, FarmCPU and SUPER) and further comparison revealed that FarmCPU is more robust in controlling both false positives and negatives as it incorporates multiple markers as covariates to eliminate confounding between testing marker and kinship. Cumulatively, a set of 22 SNPs were found to be associated with either days to first flowering (DOF), days to fifty percent flowering (DFF) or both, of which 15 were unique to trait based, 4 to PC based GWAS while 3 were shared by both. Because PC1 represents DOF, DFF and plant height (PH), four SNPs found associated to PC1 can be inferred as pleiotropic. A window of ± 2 kb of associated SNPs was aligned with available transcriptome data generated for transition from vegetative to reproductive phase in pigeonpea. Annotation analysis of these regions revealed presence of genes which might be involved in floral induction like Cytochrome p450 like Tata box binding protein, Auxin response factors, Pin like genes, F box protein, U box domain protein, chromatin remodelling complex protein, RNA methyltransferase. In summary, it appears that auxin responsive genes could be involved in regulating DOF and DFF as majority of the associated loci contained genes which are component of auxin signaling pathways in their vicinity. Overall, our findings indicates that the use of principal component analysis in GWAS is statistically more robust in terms of identifying genes and FarmCPU is a better choice compared to the other aforementioned models in dealing with both false positive and negative associations and thus can be used for traits with complex inheritance.
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Shao J, Hao Y, Wang L, Xie Y, Zhang H, Bai J, Wu J, Fu J. Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure. PLANTS (BASEL, SWITZERLAND) 2022; 11:1298. [PMID: 35631723 PMCID: PMC9144439 DOI: 10.3390/plants11101298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 05/08/2022] [Accepted: 05/09/2022] [Indexed: 06/15/2023]
Abstract
Due to insufficient identification and in-depth investigation of existing common bean germplasm resources, it is difficult for breeders to utilize these valuable genetic resources. This situation limits the breeding and industrial development of the common bean (Phaseolus vulgaris L.) in China. Genomic prediction (GP) is a breeding method that uses whole-genome molecular markers to calculate the genomic estimated breeding value (GEBV) of candidate materials and select breeding materials. This study aimed to use genomic prediction to evaluate 15 traits in a collection of 628 common bean lines (including 484 landraces and 144 breeding lines) to determine a common bean GP model. The GP model constructed by landraces showed a moderate to high predictive ability (ranging from 0.59-0.88). Using all landraces as a training set, the predictive ability of the GP model for most traits was higher than that using the landraces from each of two subgene pools, respectively. Randomly selecting breeding lines as additional training sets together with landrace training sets to predict the remaining breeding lines resulted in a higher predictive ability based on principal components analysis. This study constructed a widely applicable GP model of the common bean based on the population structure, and encouraged the development of GP models to quickly aggregate excellent traits and accelerate utilization of germplasm resources.
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Affiliation(s)
- Jing Shao
- Department of Crop Genetics and Breeding, College of Agronomy, Gansu Agricultural University, Lanzhou 730070, China;
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (Y.H.); (L.W.); (Y.X.); (H.Z.)
- Gansu Provincial Key Laboratory of Aridland Crop Science, Lanzhou 730070, China
| | - Yangfan Hao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (Y.H.); (L.W.); (Y.X.); (H.Z.)
| | - Lanfen Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (Y.H.); (L.W.); (Y.X.); (H.Z.)
| | - Yuxin Xie
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (Y.H.); (L.W.); (Y.X.); (H.Z.)
| | - Hongwei Zhang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (Y.H.); (L.W.); (Y.X.); (H.Z.)
| | - Jiangping Bai
- Department of Crop Genetics and Breeding, College of Agronomy, Gansu Agricultural University, Lanzhou 730070, China;
- Gansu Provincial Key Laboratory of Aridland Crop Science, Lanzhou 730070, China
| | - Jing Wu
- Department of Crop Genetics and Breeding, College of Agronomy, Gansu Agricultural University, Lanzhou 730070, China;
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (Y.H.); (L.W.); (Y.X.); (H.Z.)
| | - Junjie Fu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (Y.H.); (L.W.); (Y.X.); (H.Z.)
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Genotyping, the Usefulness of Imputation to Increase SNP Density, and Imputation Methods and Tools. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2467:113-138. [PMID: 35451774 DOI: 10.1007/978-1-0716-2205-6_4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Imputation has become a standard practice in modern genetic research to increase genome coverage and improve accuracy of genomic selection and genome-wide association study as a large number of samples can be genotyped at lower density (and lower cost) and, imputed up to denser marker panels or to sequence level, using information from a limited reference population. Most genotype imputation algorithms use information from relatives and population linkage disequilibrium. A number of software for imputation have been developed originally for human genetics and, more recently, for animal and plant genetics considering pedigree information and very sparse SNP arrays or genotyping-by-sequencing data. In comparison to human populations, the population structures in farmed species and their limited effective sizes allow to accurately impute high-density genotypes or sequences from very low-density SNP panels and a limited set of reference individuals. Whatever the imputation method, the imputation accuracy, measured by the correct imputation rate or the correlation between true and imputed genotypes, increased with the increasing relatedness of the individual to be imputed with its denser genotyped ancestors and as its own genotype density increased. Increasing the imputation accuracy pushes up the genomic selection accuracy whatever the genomic evaluation method. Given the marker densities, the most important factors affecting imputation accuracy are clearly the size of the reference population and the relationship between individuals in the reference and target populations.
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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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Semagn K, Iqbal M, Jarquin D, Crossa J, Howard R, Ciechanowska I, Henriquez MA, Randhawa H, Aboukhaddour R, McCallum BD, Brûlé-Babel AL, Navabi A, N'Diaye A, Pozniak C, Spaner D. Genomic Predictions for Common Bunt, FHB, Stripe Rust, Leaf Rust, and Leaf Spotting Resistance in Spring Wheat. Genes (Basel) 2022; 13:565. [PMID: 35456370 PMCID: PMC9032109 DOI: 10.3390/genes13040565] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 03/15/2022] [Accepted: 03/21/2022] [Indexed: 02/04/2023] Open
Abstract
Some studies have investigated the potential of genomic selection (GS) on stripe rust, leaf rust, Fusarium head blight (FHB), and leaf spot in wheat, but none of them have assessed the effect of the reaction norm model that incorporated GE interactions. In addition, the prediction accuracy on common bunt has not previously been studied. Here, we investigated within-population prediction accuracies using the baseline M1 model and two reaction norm models (M2 and M3) with three random cross-validation (CV1, CV2, and CV0) schemes. Three Canadian spring wheat populations were evaluated in up to eight field environments and genotyped with 3158, 5732, and 23,795 polymorphic markers. The M3 model that incorporated GE interactions reduced residual variance by an average of 10.2% as compared with the main effect M2 model and increased prediction accuracies on average by 2-6%. In some traits, the M3 model increased prediction accuracies up to 54% as compared with the M2 model. The average prediction accuracies of the M3 model with CV1, CV2, and CV0 schemes varied from 0.02 to 0.48, from 0.25 to 0.84, and from 0.14 to 0.87, respectively. In both CV2 and CV0 schemes, stripe rust in all three populations, common bunt and leaf rust in two populations, as well as FHB severity, FHB index, and leaf spot in one population had high to very high (0.54-0.87) prediction accuracies. This is the first comprehensive genomic selection study on five major diseases in spring wheat.
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Affiliation(s)
- Kassa Semagn
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - Muhammad Iqbal
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - Diego Jarquin
- Agronomy Department, University of Florida, Gainesville, FL 32611, USA
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, Mexico 06600, Mexico
| | - Reka Howard
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
| | - Izabela Ciechanowska
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - Maria Antonia Henriquez
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, 101 Route 100, Morden, MB R6M 1Y5, Canada
| | - Harpinder Randhawa
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB T1J 4B1, Canada
| | - Reem Aboukhaddour
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB T1J 4B1, Canada
| | - Brent D McCallum
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, 101 Route 100, Morden, MB R6M 1Y5, Canada
| | - Anita L Brûlé-Babel
- Department of Plant Science, University of Manitoba, 66 Dafoe Road, Winnipeg, MB R3T 2N2, Canada
| | - Alireza Navabi
- Department of Plant Agriculture, Crop Science Building, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Amidou N'Diaye
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK S7N 5A8, Canada
| | - Curtis Pozniak
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK S7N 5A8, Canada
| | - Dean Spaner
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB T6G 2P5, Canada
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Mahmood Z, Ali M, Mirza JI, Fayyaz M, Majeed K, Naeem MK, Aziz A, Trethowan R, Ogbonnaya FC, Poland J, Quraishi UM, Hickey LT, Rasheed A, He Z. Genome-Wide Association and Genomic Prediction for Stripe Rust Resistance in Synthetic-Derived Wheats. FRONTIERS IN PLANT SCIENCE 2022; 13:788593. [PMID: 35283883 PMCID: PMC8908430 DOI: 10.3389/fpls.2022.788593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
Stripe rust caused by Puccnina striiformis (Pst) is an economically important disease attacking wheat all over the world. Identifying and deploying new genes for Pst resistance is an economical and long-term strategy for controlling Pst. A genome-wide association study (GWAS) using single nucleotide polymorphisms (SNPs) and functional haplotypes were used to identify loci associated with stripe rust resistance in synthetic-derived (SYN-DER) wheats in four environments. In total, 92 quantitative trait nucleotides (QTNs) distributed over 65 different loci were associated with resistance to Pst at seedling and adult plant stages. Nine additional loci were discovered by the linkage disequilibrium-based haplotype-GWAS approach. The durable rust-resistant gene Lr34/Yr18 provided resistance in all four environments, and against all the five Pst races used in this study. The analysis identified several SYN-DER accessions that carried major genes: either Yr24/Yr26 or Yr32. New loci were also identified on chr2B, chr5B, and chr7D, and 14 QTNs and three haplotypes identified on the D-genome possibly carry new alleles of the known genes contributed by the Ae. tauschii founders. We also evaluated eleven different models for genomic prediction of Pst resistance, and a prediction accuracy up to 0.85 was achieved for an adult plant resistance, however, genomic prediction for seedling resistance remained very low. A meta-analysis based on a large number of existing GWAS would enhance the identification of new genes and loci for stripe rust resistance in wheat. The genetic framework elucidated here for stripe rust resistance in SYN-DER identified the novel loci for resistance to Pst assembled in adapted genetic backgrounds.
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Affiliation(s)
- Zahid Mahmood
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad, Pakistan
- Crop Sciences Institute, National Agricultural Research Centre (NARC), Islamabad, Pakistan
| | - Mohsin Ali
- Institute of Crop Sciences, CIMMYT-China office, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | | | | | - Khawar Majeed
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Muhammad Kashif Naeem
- National Institute for Genomics and Advanced Biotechnology (NIGAB), National Agriculture Research Center (NARC), Islamabad, Pakistan
| | - Abdul Aziz
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad, Pakistan
| | - Richard Trethowan
- Plant Breeding Institute, School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, Australia
| | | | - Jesse Poland
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | | | - Lee Thomas Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Saint Lucia, QLD, Australia
| | - Awais Rasheed
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad, Pakistan
- Institute of Crop Sciences, CIMMYT-China office, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Zhonghu He
- Institute of Crop Sciences, CIMMYT-China office, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
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Rio S, Akdemir D, Carvalho T, Sánchez JIY. Assessment of genomic prediction reliability and optimization of experimental designs in multi-environment trials. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:405-419. [PMID: 34807267 PMCID: PMC8866390 DOI: 10.1007/s00122-021-03972-2] [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: 04/24/2021] [Accepted: 10/08/2021] [Indexed: 06/13/2023]
Abstract
New forms of the coefficient of determination can help to forecast the accuracy of genomic prediction and optimize experimental designs in multi-environment trials with genotype-by-environment interactions. In multi-environment trials, the relative performance of genotypes may vary depending on the environmental conditions, and this phenomenon is commonly referred to as genotype-by-environment interaction (G[Formula: see text]E). With genomic prediction, G[Formula: see text]E can be accounted for by modeling the genetic covariance between trials, even when the overall experimental design is highly unbalanced between trials, thanks to the genomic relationship between genotypes. In this study, we propose new forms of the coefficient of determination (CD, i.e., the expected model-based square correlation between a genetic value and its corresponding prediction) that can be used to forecast the genomic prediction reliability of genotypes, both for their trial-specific performance and their mean performance. As the expected prediction reliability based on these new CD criteria is generally a good approximation of the observed reliability, we demonstrate that they can be used to optimize multi-environment trials in the presence of G[Formula: see text]E. In addition, this reliability may be highly variable between genotypes, especially in unbalanced designs with complex pedigree relationships between genotypes. Therefore, it can be useful for breeders to assess it before selecting genotypes based on their predicted genetic values. Using a wheat population evaluated both for simulated and phenology traits, and two maize populations evaluated for grain yield, we illustrate this approach and confirm the value of our new CD criteria.
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Affiliation(s)
- Simon Rio
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA) Campus de Montegancedo-UPM, 28223 Pozuelo de Alarcón Madrid, Spain
| | - Deniz Akdemir
- CIBMTR (Center for International Blood and Marrow Transplant Research), National Marrow Donor Program/Be The Match, Minneapolis, MN USA
| | - Tiago Carvalho
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA) Campus de Montegancedo-UPM, 28223 Pozuelo de Alarcón Madrid, Spain
| | - Julio Isidro y Sánchez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA) Campus de Montegancedo-UPM, 28223 Pozuelo de Alarcón Madrid, Spain
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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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36
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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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Gholami M, Wimmer V, Sansaloni C, Petroli C, Hearne SJ, Covarrubias-Pazaran G, Rensing S, Heise J, Pérez-Rodríguez P, Dreisigacker S, Crossa J, Martini JWR. A Comparison of the Adoption of Genomic Selection Across Different Breeding Institutions. FRONTIERS IN PLANT SCIENCE 2021; 12:728567. [PMID: 34868114 PMCID: PMC8640095 DOI: 10.3389/fpls.2021.728567] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 09/30/2021] [Indexed: 06/13/2023]
Affiliation(s)
| | | | - Carolina Sansaloni
- Genetic Resources Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
| | - Cesar Petroli
- Genetic Resources Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
| | - Sarah J. Hearne
- Genetic Resources Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
- Excellence in Breeding Platform, Consultative Group for International Agricultural Research, Texcoco, Mexico
| | | | - Stefan Rensing
- IT Solutions for Animal Production (vit - Vereinigte Informationssysteme Tierhaltung w.V.), Verden, Germany
| | - Johannes Heise
- IT Solutions for Animal Production (vit - Vereinigte Informationssysteme Tierhaltung w.V.), Verden, Germany
| | | | - Susanne Dreisigacker
- Global Wheat Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
| | - José Crossa
- Genetic Resources Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
- Department of Statistics, Colegio de Postgraduados, Montecillos, Mexico
| | - Johannes W. R. Martini
- Genetic Resources Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
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Ubbens J, Parkin I, Eynck C, Stavness I, Sharpe AG. Deep neural networks for genomic prediction do not estimate marker effects. THE PLANT GENOME 2021; 14:e20147. [PMID: 34596363 DOI: 10.1002/tpg2.20147] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 07/09/2021] [Indexed: 06/13/2023]
Abstract
Genomic prediction is a promising technology for advancing both plant and animal breeding, with many different prediction models evaluated in the literature. It has been suggested that the ability of powerful nonlinear models, such as deep neural networks, to capture complex epistatic effects between markers offers advantages for genomic prediction. However, these methods tend not to outperform classical linear methods, leaving it an open question why this capacity to model nonlinear effects does not seem to result in better predictive capability. In this work, we propose the theory that, because of a previously described principle called shortcut learning, deep neural networks tend to base their predictions on overall genetic relatedness rather than on the effects of particular markers such as epistatic effects. Using several datasets of crop plants [lentil (Lens culinaris Medik.), wheat (Triticum aestivum L.), and Brassica carinata A. Braun], we demonstrate the network's indifference to the values of the markers by showing that the same network, provided with only the locations of matches between markers for two individuals, is able to perform prediction to the same level of accuracy.
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Affiliation(s)
- Jordan Ubbens
- Global Institute for Food Security (GIFS), University of Saskatchewan, Saskatoon, SK, S7N 0W9, Canada
| | - Isobel Parkin
- Agriculture and Agri-Food Canada, Saskatoon, SK, S7N 0X2, Canada
| | - Christina Eynck
- Agriculture and Agri-Food Canada, Saskatoon, SK, S7N 0X2, Canada
| | - Ian Stavness
- Global Institute for Food Security (GIFS), University of Saskatchewan, Saskatoon, SK, S7N 0W9, Canada
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, S7N 0W9, Canada
| | - Andrew G Sharpe
- Global Institute for Food Security (GIFS), University of Saskatchewan, Saskatoon, SK, S7N 0W9, Canada
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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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Michel S, Löschenberger F, Ametz C, Bürstmayr H. Genomic selection of parents and crosses beyond the native gene pool of a breeding program. THE PLANT GENOME 2021; 14:e20153. [PMID: 34651462 DOI: 10.1002/tpg2.20153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 08/03/2021] [Indexed: 06/13/2023]
Abstract
Genomic selection has become a valuable tool for selecting cultivar candidates in many plant breeding programs. Genomic selection of elite parents and crossing combinations with germplasm developed outside a breeding program has, however, hardly been explored until now. The aim of this study was to assess the potential of this method for commonly ranking and selecting elite germplasm developed within and beyond a given breeding program. A winter wheat (Triticum aestivum L.) population consisting of 611 in-house and 87 externally developed lines was used to compare training population compositions and statistical models for genomically predicting baking quality in this framework. Augmenting training populations with lines from other breeding programs had a larger influence on the prediction ability than adding in-house generated lines when aiming to commonly rank both germplasm sets. Exploiting preexisting information of secondary correlated traits resulted likewise in more accurate predictions both in empirical analyses and simulations. Genotyping germplasm developed beyond a given breeding program is moreover a convenient way to clarify its relationships with a breeder's own germplasm because pedigree information is oftentimes not available for this purpose. Genomic predictions can thus support a more informed diversity management, especially when integrating simply to phenotype correlated traits to partly circumvent resource reallocations for a costly phenotyping of germplasm from other programs.
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Affiliation(s)
- Sebastian Michel
- Dep. of Agrobiotechnology, IFA-Tulln, Univ. of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430 Tulln, Austria
| | | | - Christian Ametz
- Saatzucht Donau GesmbH & CoKG, Saatzuchtstrasse 11, 2301 Probstdorf, Austria
| | - Hermann Bürstmayr
- Dep. of Agrobiotechnology, IFA-Tulln, Univ. of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430 Tulln, Austria
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Bellucci E, Mario Aguilar O, Alseekh S, Bett K, Brezeanu C, Cook D, De la Rosa L, Delledonne M, Dostatny DF, Ferreira JJ, Geffroy V, Ghitarrini S, Kroc M, Kumar Agrawal S, Logozzo G, Marino M, Mary‐Huard T, McClean P, Meglič V, Messer T, Muel F, Nanni L, Neumann K, Servalli F, Străjeru S, Varshney RK, Vasconcelos MW, Zaccardelli M, Zavarzin A, Bitocchi E, Frontoni E, Fernie AR, Gioia T, Graner A, Guasch L, Prochnow L, Oppermann M, Susek K, Tenaillon M, Papa R. The INCREASE project: Intelligent Collections of food-legume genetic resources for European agrofood systems. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2021; 108:646-660. [PMID: 34427014 PMCID: PMC9293105 DOI: 10.1111/tpj.15472] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 08/11/2021] [Accepted: 08/17/2021] [Indexed: 05/14/2023]
Abstract
Food legumes are crucial for all agriculture-related societal challenges, including climate change mitigation, agrobiodiversity conservation, sustainable agriculture, food security and human health. The transition to plant-based diets, largely based on food legumes, could present major opportunities for adaptation and mitigation, generating significant co-benefits for human health. The characterization, maintenance and exploitation of food-legume genetic resources, to date largely unexploited, form the core development of both sustainable agriculture and a healthy food system. INCREASE will implement, on chickpea (Cicer arietinum), common bean (Phaseolus vulgaris), lentil (Lens culinaris) and lupin (Lupinus albus and L. mutabilis), a new approach to conserve, manage and characterize genetic resources. Intelligent Collections, consisting of nested core collections composed of single-seed descent-purified accessions (i.e., inbred lines), will be developed, exploiting germplasm available both from genebanks and on-farm and subjected to different levels of genotypic and phenotypic characterization. Phenotyping and gene discovery activities will meet, via a participatory approach, the needs of various actors, including breeders, scientists, farmers and agri-food and non-food industries, exploiting also the power of massive metabolomics and transcriptomics and of artificial intelligence and smart tools. Moreover, INCREASE will test, with a citizen science experiment, an innovative system of conservation and use of genetic resources based on a decentralized approach for data management and dynamic conservation. By promoting the use of food legumes, improving their quality, adaptation and yield and boosting the competitiveness of the agriculture and food sector, the INCREASE strategy will have a major impact on economy and society and represents a case study of integrative and participatory approaches towards conservation and exploitation of crop genetic resources.
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Affiliation(s)
- Elisa Bellucci
- Department of Agricultural, Food and Environmental SciencesPolytechnic University of Marchevia Brecce BiancheAncona60131Italy
| | - Orlando Mario Aguilar
- Instituto de Biotecnología y Biología MolecularUNLP‐CONICETCCT La PlataLa PlataArgentina
| | - Saleh Alseekh
- Max‐Planck‐Institute of Molecular Plant PhysiologyAm MüePotsdam‐Golm14476Germany
- Centre of Plant Systems Biology and BiotechnologyPlovdiv4000Bulgaria
| | - Kirstin Bett
- Department of Plant SciencesUniversity of Saskatchewan51 Campus DriveSaskatoonSKS7N 5A8Canada
| | - Creola Brezeanu
- Staţiunea de Cercetare Dezvoltare Pentru LegumiculturăBacău600388Romania
| | - Douglas Cook
- Department of Plant PathologyUniversity of California DavisDavisCA95616‐8680USA
| | - Lucía De la Rosa
- Spanish Plant Genetic Resources National Center (INIA, CRF)National Institute for Agricultural and Food Research and TechnologyAlcalá de HenaresMadrid28800Spain
| | - Massimo Delledonne
- Department of BiotechnologyUniversity of VeronaStrada Le Grazie 15Verona37134Italy
| | - Denise F. Dostatny
- National Centre for Plant Genetic Resources, Plant Breeding and Acclimatization Institute‐NRIRadzikówBłonie05‐870Poland
| | - Juan J. Ferreira
- Regional Service for Agrofood Research and Development (SERIDA)Ctra AS‐267, PK 19VillaviciosaAsturias33300Spain
| | - Valérie Geffroy
- CNRSINRAEInstitute of Plant Sciences Paris‐Saclay (IPS2)Univ EvryUniversité Paris‐SaclayOrsay91405France
- CNRSINRAEInstitute of Plant Sciences Paris Saclay (IPS2)Université de ParisOrsay91405France
| | | | - Magdalena Kroc
- Legume Genomics TeamInstitute of Plant GeneticsPolish Academy of SciencesStrzeszynska 34Poznan60‐479Poland
| | - Shiv Kumar Agrawal
- Genetic Resources SectionInternational Center for Agricultural Research in the Dry AreasICARDAAgdal RabatMorocco
| | - Giuseppina Logozzo
- School of Agricultural, Forestry, Food and Environmental SciencesUniversity of BasilicataPotenza85100Italy
| | - Mario Marino
- International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGRFA)Food and Agriculture Organization of the United Nations (FAO)Viale delle Terme di CaracallaRome00153Italy
| | - Tristan Mary‐Huard
- INRAECNRSAgroParisTechGénétique Quantitative et Evolution ‐ Le MoulonUniversité Paris‐SaclayGif‐sur‐YvetteFrance
| | - Phil McClean
- Department of Plant Sciences, Genomics and Bioinformatics ProgramNorth Dakota State UniversityFargoND58108USA
| | - Vladimir Meglič
- Crop Science DepartmentAgricultural Institute of SloveniaHacquetova ulica 17Ljubljana1000Slovenia
| | - Tamara Messer
- EURICE ‐ European Research and Project Office GmbHHeinrich‐Hertz‐Allee 1St. Ingbert66386Germany
| | - Frédéric Muel
- Terres InoviaInstitut Technique des oléagineux, des protéagineux eu du chanvren1 Av L. BrétignièresThiverval-Grignon78850France
| | - Laura Nanni
- Department of Agricultural, Food and Environmental SciencesPolytechnic University of Marchevia Brecce BiancheAncona60131Italy
| | - Kerstin Neumann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) GaterslebenSeeland06466Germany
| | - Filippo Servalli
- Comunità del Mais Spinato di Gandino (MASP)Via XX Settembre, 5GandinoBergamo24024Italy
| | - Silvia Străjeru
- Suceava Genebank (BRGV)Bdul 1 Mai, nr. 17Suceava720224Romania
| | - Rajeev K. Varshney
- Center of Excellence in Genomics and Systems Biology (CEGSB)International Crops Research Institute for the Semi- Arid Tropics (ICRISAT)PatancheruIndia
- State Agricultural Biotechnology CentreCentre for Crop and Food InnovationFood Futures InstituteMurdoch UniversityMurdochWestern AustraliaAustralia
| | - Marta W. Vasconcelos
- CBQF – Centro de Biotecnologia e Química Fina – Laboratório AssociadoEscola Superior de BiotecnologiaUniversidade Católica PortuguesaRua Diogo Botelho 1327Porto4169-005Portugal
| | - Massimo Zaccardelli
- Council for Agricultural Research and EconomicsResearch Centre for Vegetable and Ornamental CropsVia Cavalleggeri 25Pontecagnano‐FaianoSA84098Italy
| | - Aleksei Zavarzin
- Federal Research CenterThe N.I. Vavilov All‐Russian Institute of Plant Genetic ResourcesSt. Petersburg190031Russia
| | - Elena Bitocchi
- Department of Agricultural, Food and Environmental SciencesPolytechnic University of Marchevia Brecce BiancheAncona60131Italy
| | - Emanuele Frontoni
- Department of Information EngineeringPolytechnic University of Marchevia Brecce BiancheAncona60131Italy
| | - Alisdair R. Fernie
- Max‐Planck‐Institute of Molecular Plant PhysiologyAm MüePotsdam‐Golm14476Germany
- Centre of Plant Systems Biology and BiotechnologyPlovdiv4000Bulgaria
| | - Tania Gioia
- School of Agricultural, Forestry, Food and Environmental SciencesUniversity of BasilicataPotenza85100Italy
| | - Andreas Graner
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) GaterslebenSeeland06466Germany
| | - Luis Guasch
- Spanish Plant Genetic Resources National Center (INIA, CRF)National Institute for Agricultural and Food Research and TechnologyAlcalá de HenaresMadrid28800Spain
| | - Lena Prochnow
- EURICE ‐ European Research and Project Office GmbHHeinrich‐Hertz‐Allee 1St. Ingbert66386Germany
| | - Markus Oppermann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) GaterslebenSeeland06466Germany
| | - Karolina Susek
- Legume Genomics TeamInstitute of Plant GeneticsPolish Academy of SciencesStrzeszynska 34Poznan60‐479Poland
| | - Maud Tenaillon
- INRAECNRSAgroParisTechGénétique Quantitative et Evolution ‐ Le MoulonUniversité Paris‐SaclayGif‐sur‐YvetteFrance
| | - Roberto Papa
- Department of Agricultural, Food and Environmental SciencesPolytechnic University of Marchevia Brecce BiancheAncona60131Italy
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Tomar V, Singh D, Dhillon GS, Chung YS, Poland J, Singh RP, Joshi AK, Gautam Y, Tiwari BS, Kumar U. Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat ( Triticum aestivum L.). FRONTIERS IN PLANT SCIENCE 2021; 12:720123. [PMID: 34691100 PMCID: PMC8531512 DOI: 10.3389/fpls.2021.720123] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 09/03/2021] [Indexed: 06/13/2023]
Abstract
Genomic selection (GS) has the potential to improve the selection gain for complex traits in crop breeding programs from resource-poor countries. The GS model performance in multi-environment (ME) trials was assessed for 141 advanced breeding lines under four field environments via cross-predictions. We compared prediction accuracy (PA) of two GS models with or without accounting for the environmental variation on four quantitative traits of significant importance, i.e., grain yield (GRYLD), thousand-grain weight, days to heading, and days to maturity, under North and Central Indian conditions. For each trait, we generated PA using the following two different ME cross-validation (CV) schemes representing actual breeding scenarios: (1) predicting untested lines in tested environments through the ME model (ME_CV1) and (2) predicting tested lines in untested environments through the ME model (ME_CV2). The ME predictions were compared with the baseline single-environment (SE) GS model (SE_CV1) representing a breeding scenario, where relationships and interactions are not leveraged across environments. Our results suggested that the ME models provide a clear advantage over SE models in terms of robust trait predictions. Both ME models provided 2-3 times higher prediction accuracies for all four traits across the four tested environments, highlighting the importance of accounting environmental variance in GS models. While the improvement in PA from SE to ME models was significant, the CV1 and CV2 schemes did not show any clear differences within ME, indicating the ME model was able to predict the untested environments and lines equally well. Overall, our results provide an important insight into the impact of environmental variation on GS in smaller breeding programs where these programs can potentially increase the rate of genetic gain by leveraging the ME wheat breeding trials.
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Affiliation(s)
- Vipin Tomar
- Borlaug Institute for South Asia, Ludhiana, India
- Department of Biological Sciences and Biotechnology, Institute of Advanced Research, Gandhinagar, India
- International Maize and Wheat Improvement Center, New Delhi, India
| | - Daljit Singh
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Guriqbal Singh Dhillon
- Department of Biotechnology, Thapar Institute of Engineering & Technology, Patiala, India
| | - Yong Suk Chung
- Department of Plant Resources and Environment, Jeju National University, Jeju-si, South Korea
| | - Jesse Poland
- Department of Plant Pathology, Kansas State University, Manhattan, KS, United States
| | - Ravi Prakash Singh
- Global Wheat Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
| | - Arun Kumar Joshi
- Borlaug Institute for South Asia, Ludhiana, India
- International Maize and Wheat Improvement Center, New Delhi, India
- Global Wheat Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
| | | | - Budhi Sagar Tiwari
- Department of Biological Sciences and Biotechnology, Institute of Advanced Research, Gandhinagar, India
| | - Uttam Kumar
- Borlaug Institute for South Asia, Ludhiana, India
- International Maize and Wheat Improvement Center, New Delhi, India
- Global Wheat Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
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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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Engels JMM, Ebert AW. A Critical Review of the Current Global Ex Situ Conservation System for Plant Agrobiodiversity. II. Strengths and Weaknesses of the Current System and Recommendations for Its Improvement. PLANTS (BASEL, SWITZERLAND) 2021; 10:1904. [PMID: 34579439 PMCID: PMC8472064 DOI: 10.3390/plants10091904] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 09/05/2021] [Accepted: 09/09/2021] [Indexed: 02/08/2023]
Abstract
In this paper, we review gene bank operations that have an influence on the global conservation system, with the intention to identify critical aspects that should be improved for optimum performance. We describe the role of active and base collections and the importance of linking germplasm conservation and use, also in view of new developments in genomics and phenomics that facilitate more effective and efficient conservation and use of plant agrobiodiversity. Strengths, limitations, and opportunities of the existing global ex situ conservation system are discussed, and measures are proposed to achieve a rational, more effective, and efficient global system for germplasm conservation and sustainable use. The proposed measures include filling genetic and geographic gaps in current ex situ collections; determining unique accessions at the global level for long-term conservation in virtual base collections; intensifying existing international collaborations among gene banks and forging collaborations with the botanic gardens community; increasing investment in conservation research and user-oriented supportive research; improved accession-level description of the genetic diversity of crop collections; improvements of the legal and policy framework; and oversight of the proposed network of global base collections.
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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: 4] [Impact Index Per Article: 1.3] [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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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: 154] [Impact Index Per Article: 51.3] [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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47
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Cortés AJ, López-Hernández F. Harnessing Crop Wild Diversity for Climate Change Adaptation. Genes (Basel) 2021; 12:783. [PMID: 34065368 PMCID: PMC8161384 DOI: 10.3390/genes12050783] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/28/2021] [Accepted: 05/19/2021] [Indexed: 12/20/2022] Open
Abstract
Warming and drought are reducing global crop production with a potential to substantially worsen global malnutrition. As with the green revolution in the last century, plant genetics may offer concrete opportunities to increase yield and crop adaptability. However, the rate at which the threat is happening requires powering new strategies in order to meet the global food demand. In this review, we highlight major recent 'big data' developments from both empirical and theoretical genomics that may speed up the identification, conservation, and breeding of exotic and elite crop varieties with the potential to feed humans. We first emphasize the major bottlenecks to capture and utilize novel sources of variation in abiotic stress (i.e., heat and drought) tolerance. We argue that adaptation of crop wild relatives to dry environments could be informative on how plant phenotypes may react to a drier climate because natural selection has already tested more options than humans ever will. Because isolated pockets of cryptic diversity may still persist in remote semi-arid regions, we encourage new habitat-based population-guided collections for genebanks. We continue discussing how to systematically study abiotic stress tolerance in these crop collections of wild and landraces using geo-referencing and extensive environmental data. By uncovering the genes that underlie the tolerance adaptive trait, natural variation has the potential to be introgressed into elite cultivars. However, unlocking adaptive genetic variation hidden in related wild species and early landraces remains a major challenge for complex traits that, as abiotic stress tolerance, are polygenic (i.e., regulated by many low-effect genes). Therefore, we finish prospecting modern analytical approaches that will serve to overcome this issue. Concretely, genomic prediction, machine learning, and multi-trait gene editing, all offer innovative alternatives to speed up more accurate pre- and breeding efforts toward the increase in crop adaptability and yield, while matching future global food demands in the face of increased heat and drought. In order for these 'big data' approaches to succeed, we advocate for a trans-disciplinary approach with open-source data and long-term funding. The recent developments and perspectives discussed throughout this review ultimately aim to contribute to increased crop adaptability and yield in the face of heat waves and drought events.
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Affiliation(s)
- Andrés J. Cortés
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Km 7 Vía Rionegro, Las Palmas, Rionegro 054048, Colombia;
- Departamento de Ciencias Forestales, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, Sede Medellín, Medellín 050034, Colombia
| | - Felipe López-Hernández
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Km 7 Vía Rionegro, Las Palmas, Rionegro 054048, Colombia;
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48
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David O, Le Rouzic A, Dillmann C. Optimization of sampling designs for pedigrees and association studies. Biometrics 2021; 78:1056-1066. [PMID: 33876835 DOI: 10.1111/biom.13476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 03/10/2021] [Accepted: 04/02/2021] [Indexed: 11/29/2022]
Abstract
In many studies, related individuals are phenotyped in order to infer how their genotype contributes to their phenotype, through the estimation of parameters such as breeding values or locus effects. When it is not possible to phenotype all the individuals, it is important to properly sample the population to improve the precision of the statistical analysis. This article studies how to optimize such sampling designs for pedigrees and association studies. Two sampling methods are developed, stratified sampling and D optimality. It is found that it is important to take account of mutation when sampling pedigrees with many generations: as the size of mutation effects increases, optimized designs sample more individuals in late generations. Optimized designs for association studies tend to improve the joint estimation of breeding values and locus effects, all the more as sample size is low and the genetic architecture of the trait is simple. When the trait is determined by few loci, they are reminiscent of classical experimental designs for regression models and tend to select homozygous individuals. When the trait is determined by many loci, locus effects may be difficult to estimate, even if an optimized design is used.
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Affiliation(s)
- Olivier David
- Université Paris-Saclay, INRAE, MaIAGE, 78350, Jouy-en-Josas, France
| | - Arnaud Le Rouzic
- Université Paris-Saclay, CNRS, IRD, Évolution, Génomes, Comportement, Écologie, 91198, Gif-sur-Yvette, France
| | - Christine Dillmann
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
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49
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An Overview of Key Factors Affecting Genomic Selection for Wheat Quality Traits. PLANTS 2021; 10:plants10040745. [PMID: 33920359 PMCID: PMC8069980 DOI: 10.3390/plants10040745] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/06/2021] [Accepted: 04/08/2021] [Indexed: 11/17/2022]
Abstract
Selection for wheat (Triticum aestivum L.) grain quality is often costly and time-consuming since it requires extensive phenotyping in the last phases of development of new lines and cultivars. The development of high-throughput genotyping in the last decade enabled reliable and rapid predictions of breeding values based only on marker information. Genomic selection (GS) is a method that enables the prediction of breeding values of individuals by simultaneously incorporating all available marker information into a model. The success of GS depends on the obtained prediction accuracy, which is influenced by various molecular, genetic, and phenotypic factors, as well as the factors of the selected statistical model. The objectives of this article are to review research on GS for wheat quality done so far and to highlight the key factors affecting prediction accuracy, in order to suggest the most applicable approach in GS for wheat quality traits.
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50
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Tehseen MM, Kehel Z, Sansaloni CP, Lopes MDS, Amri A, Kurtulus E, Nazari K. Comparison of Genomic Prediction Methods for Yellow, Stem, and Leaf Rust Resistance in Wheat Landraces from Afghanistan. PLANTS 2021; 10:plants10030558. [PMID: 33809650 PMCID: PMC8001917 DOI: 10.3390/plants10030558] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 02/28/2021] [Accepted: 03/13/2021] [Indexed: 11/16/2022]
Abstract
Wheat rust diseases, including yellow rust (Yr; also known as stripe rust) caused by Puccinia striiformis Westend. f. sp. tritici, leaf rust (Lr) caused by Puccinia triticina Eriks. and stem rust (Sr) caused by Puccinia graminis Pres f. sp. tritici are major threats to wheat production all around the globe. Durable resistance to wheat rust diseases can be achieved through genomic-assisted prediction of resistant accessions to increase genetic gain per unit time. Genomic prediction (GP) is a promising technology that uses genomic markers to estimate genomic-assisted breeding values (GBEVs) for selecting resistant plant genotypes and accumulating favorable alleles for adult plant resistance (APR) to wheat rust diseases. To evaluate GP we compared the predictive ability of nine different parametric, semi-parametric and Bayesian models including Genomic Unbiased Linear Prediction (GBLUP), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (EN), Bayesian Ridge Regression (BRR), Bayesian A (BA), Bayesian B (BB), Bayesian C (BC) and Reproducing Kernel Hilbert Spacing model (RKHS) to estimate GEBV’s for APR to yellow, leaf and stem rust of wheat in a panel of 363 bread wheat landraces of Afghanistan origin. Based on five-fold cross validation the mean predictive abilities were 0.33, 0.30, 0.38, and 0.33 for Yr (2016), Yr (2017), Lr, and Sr, respectively. No single model outperformed the rest of the models for all traits. LASSO and EN showed the lowest predictive ability in four of the five traits. GBLUP and RR gave similar predictive abilities, whereas Bayesian models were not significantly different from each other as well. We also investigated the effect of the number of genotypes and the markers used in the analysis on the predictive ability of the GP model. The predictive ability was highest with 1000 markers and there was a linear trend in the predictive ability and the size of the training population. The results of the study are encouraging, confirming the feasibility of GP to be effectively applied in breeding programs for resistance to all three wheat rust diseases.
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Affiliation(s)
| | - Zakaria Kehel
- International Center for Agricultural Research in the Dry Areas (ICARDA), ICARDA-PreBreeding & Genebank Operations, Biodiversity and Crop Improvement Program, P.O. Box 10000 Rabat, Morocco; (Z.K.); (A.A.)
| | - Carolina P. Sansaloni
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz Km. 45, El Batán, Texcoco C.P. 56237, Mexico;
| | - Marta da Silva Lopes
- Sustainable Field Crops Programme, IRTA (Institute for Food and Agricultural Research and Technology), 25198 Lleida, Spain;
| | - Ahmed Amri
- International Center for Agricultural Research in the Dry Areas (ICARDA), ICARDA-PreBreeding & Genebank Operations, Biodiversity and Crop Improvement Program, P.O. Box 10000 Rabat, Morocco; (Z.K.); (A.A.)
| | - Ezgi Kurtulus
- International Center for Agricultural Research in the Dry Areas (ICARDA), Biodiversity and Crop Improvement Program, Regional Cereal Rust Research Center (RCRRC), P.O. Box 35661 Menemen, Izmir, Turkey;
| | - Kumarse Nazari
- International Center for Agricultural Research in the Dry Areas (ICARDA), Biodiversity and Crop Improvement Program, Regional Cereal Rust Research Center (RCRRC), P.O. Box 35661 Menemen, Izmir, Turkey;
- Correspondence:
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