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Laurençon M, Legrix J, Wagner MH, Demilly D, Baron C, Rolland S, Ducournau S, Laperche A, Nesi N. Genomic and phenomic predictions help capture low-effect alleles promoting seed germination in oilseed rape in addition to QTL analyses. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:156. [PMID: 38858297 PMCID: PMC11164772 DOI: 10.1007/s00122-024-04659-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 05/25/2024] [Indexed: 06/12/2024]
Abstract
KEY MESSAGE Phenomic prediction implemented on a large diversity set can efficiently predict seed germination, capture low-effect favorable alleles that are not revealed by GWAS and identify promising genetic resources. Oilseed rape faces many challenges, especially at the beginning of its developmental cycle. Achieving rapid and uniform seed germination could help to ensure a successful establishment and therefore enabling the crop to compete with weeds and tolerate stresses during the earliest developmental stages. The polygenic nature of seed germination was highlighted in several studies, and more knowledge is needed about low- to moderate-effect underlying loci in order to enhance seed germination effectively by improving the genetic background and incorporating favorable alleles. A total of 17 QTL were detected for seed germination-related traits, for which the favorable alleles often corresponded to the most frequent alleles in the panel. Genomic and phenomic predictions methods provided moderate-to-high predictive abilities, demonstrating the ability to capture small additive and non-additive effects for seed germination. This study also showed that phenomic prediction estimated phenotypic values closer to phenotypic values than GEBV. Finally, as the predictive ability of phenomic prediction was less influenced by the genetic structure of the panel, it is worth using this prediction method to characterize genetic resources, particularly with a view to design prebreeding populations.
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Affiliation(s)
- Marianne Laurençon
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
| | - Julie Legrix
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
| | - Marie-Hélène Wagner
- Groupe d'Etude et de Contrôle des Variétés Et des Semences (GEVES), 49070, Beaucouzé, France
| | - Didier Demilly
- Groupe d'Etude et de Contrôle des Variétés Et des Semences (GEVES), 49070, Beaucouzé, France
| | - Cécile Baron
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
| | - Sophie Rolland
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
| | - Sylvie Ducournau
- Groupe d'Etude et de Contrôle des Variétés Et des Semences (GEVES), 49070, Beaucouzé, France
| | - Anne Laperche
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France.
| | - Nathalie Nesi
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
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Bose S, Banerjee S, Kumar S, Saha A, Nandy D, Hazra S. Review of applications of artificial intelligence (AI) methods in crop research. J Appl Genet 2024; 65:225-240. [PMID: 38216788 DOI: 10.1007/s13353-023-00826-z] [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: 08/13/2023] [Revised: 12/23/2023] [Accepted: 12/26/2023] [Indexed: 01/14/2024]
Abstract
Sophisticated and modern crop improvement techniques can bridge the gap for feeding the ever-increasing population. Artificial intelligence (AI) refers to the simulation of human intelligence in machines, which refers to the application of computational algorithms, machine learning (ML) and deep learning (DL) techniques. This is aimed to generalise patterns and relationships from historical data, employing various mathematical optimisation techniques thus making prediction models for facilitating selection of superior genotypes. These techniques are less resource intensive and can solve the problem based on the analysis of large-scale phenotypic datasets. ML for genomic selection (GS) uses high-throughput genotyping technologies to gather genetic information on a large number of markers across the genome. The prediction of GS models is based on the mathematical relation between genotypic and phenotypic data from the training population. ML techniques have emerged as powerful tools for genome editing through analysing large-scale genomic data and facilitating the development of accurate prediction models. Precise phenotyping is a prerequisite to advance crop breeding for solving agricultural production-related issues. ML algorithms can solve this problem through generating predictive models, based on the analysis of large-scale phenotypic datasets. DL models also have the potential reliability of precise phenotyping. This review provides a comprehensive overview on various ML and DL models, their applications, potential to enhance the efficiency, specificity and safety towards advanced crop improvement protocols such as genomic selection, genome editing, along with phenotypic prediction to promote accelerated breeding.
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Affiliation(s)
- Suvojit Bose
- Department of Vegetables and Spice Crops, Uttar Banga Krishi Viswavidyalaya, Pundibari, Cooch Behar, 736165, West Bengal, India
| | | | - Soumya Kumar
- School of Agricultural Sciences, JIS University, Kolkata, 700109, West Bengal, India
| | - Akash Saha
- School of Agricultural Sciences, JIS University, Kolkata, 700109, West Bengal, India
| | - Debalina Nandy
- School of Agricultural Sciences, JIS University, Kolkata, 700109, West Bengal, India
| | - Soham Hazra
- Department of Agriculture, Brainware University, Barasat, 700125, West Bengal, India.
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Zhou C, Jiang W, Guo J, Zhu L, Liu L, Liu S, Chen R, Du B, Huang J. Genome-wide association study and genomic prediction for resistance to brown planthopper in rice. FRONTIERS IN PLANT SCIENCE 2024; 15:1373081. [PMID: 38576786 PMCID: PMC10991774 DOI: 10.3389/fpls.2024.1373081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 03/08/2024] [Indexed: 04/06/2024]
Abstract
The brown planthopper (BPH) is the most destructive insect pest that threatens rice production globally. Developing rice varieties incorporating BPH-resistant genes has proven to be an effective control measure against BPH. In this study, we assessed the resistance of a core collection consisting of 502 rice germplasms by evaluating resistance scores, weight gain rates and honeydew excretions. A total of 117 rice varieties (23.31%) exhibited resistance to BPH. Genome-wide association studies (GWAS) were performed on both the entire panel of 502 rice varieties and its subspecies, and 6 loci were significantly associated with resistance scores (P value < 1.0e-8). Within these loci, we identified eight candidate genes encoding receptor-like protein kinase (RLK), nucleotide-binding and leucine-rich repeat (NB-LRR), or LRR proteins. Two loci had not been detected in previous study and were entirely novel. Furthermore, we evaluated the predictive ability of genomic selection for resistance to BPH. The results revealed that the highest prediction accuracy for BPH resistance reached 0.633. As expected, the prediction accuracy increased progressively with an increasing number of SNPs, and a total of 6.7K SNPs displayed comparable accuracy to 268K SNPs. Among various statistical models tested, the random forest model exhibited superior predictive accuracy. Moreover, increasing the size of training population improved prediction accuracy; however, there was no significant difference in prediction accuracy between a training population size of 737 and 1179. Additionally, when there existed close genetic relatedness between the training and validation populations, higher prediction accuracies were observed compared to scenarios when they were genetically distant. These findings provide valuable resistance candidate genes and germplasm resources and are crucial for the application of genomic selection for breeding durable BPH-resistant rice varieties.
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Affiliation(s)
- Cong Zhou
- Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences/The Key Laboratory of Biology and Genetic Improvement of Oil Crops, The Ministry of Agriculture and Rural Affairs, Wuhan, China
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, China
| | - Weihua Jiang
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, China
| | - Jianping Guo
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, China
| | - Lili Zhu
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, China
| | - Lijiang Liu
- Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences/The Key Laboratory of Biology and Genetic Improvement of Oil Crops, The Ministry of Agriculture and Rural Affairs, Wuhan, China
| | - Shengyi Liu
- Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences/The Key Laboratory of Biology and Genetic Improvement of Oil Crops, The Ministry of Agriculture and Rural Affairs, Wuhan, China
| | - Rongzhi Chen
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, China
| | - Bo Du
- State Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, China
| | - Jin Huang
- Cash Crops Research Institute, Hubei Academy of Agricultural Sciences, Wuhan, China
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Maggiorelli A, Baig N, Prigge V, Bruckmüller J, Stich B. Using drone-retrieved multispectral data for phenomic selection in potato breeding. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:70. [PMID: 38446220 PMCID: PMC10917832 DOI: 10.1007/s00122-024-04567-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 01/30/2024] [Indexed: 03/07/2024]
Abstract
Predictive breeding approaches, like phenomic or genomic selection, have the potential to increase the selection gain for potato breeding programs which are characterized by very large numbers of entries in early stages and the availability of very few tubers per entry in these stages. The objectives of this study were to (i) explore the capabilities of phenomic prediction based on drone-derived multispectral reflectance data in potato breeding by testing different prediction scenarios on a diverse panel of tetraploid potato material from all market segments and considering a broad range of traits, (ii) compare the performance of phenomic and genomic predictions, and (iii) assess the predictive power of mixed relationship matrices utilizing weighted SNP array and multispectral reflectance data. Predictive abilities of phenomic prediction scenarios varied greatly within a range of - 0.15 and 0.88 and were strongly dependent on the environment, predicted trait, and considered prediction scenario. We observed high predictive abilities with phenomic prediction for yield (0.45), maturity (0.88), foliage development (0.73), and emergence (0.73), while all other traits achieved higher predictive ability with genomic compared to phenomic prediction. When a mixed relationship matrix was used for prediction, higher predictive abilities were observed for 20 out of 22 traits, showcasing that phenomic and genomic data contained complementary information. We see the main application of phenomic selection in potato breeding programs to allow for the use of the principle of predictive breeding in the pot seedling or single hill stage where genotyping is not recommended due to high costs.
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Affiliation(s)
- Alessio Maggiorelli
- Institute of Quantitative Genetics and Genomics of Plants (QGGP), Heinrich-Heine-University, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Nadia Baig
- Institute of Quantitative Genetics and Genomics of Plants (QGGP), Heinrich-Heine-University, Universitätsstraße 1, 40225, Düsseldorf, Germany
| | - Vanessa Prigge
- SaKa Pflanzenzucht GmbH & Co. KG, Eichenallee 9, 24340, Windeby, Germany
| | - Julien Bruckmüller
- SaKa Pflanzenzucht GmbH & Co. KG, Eichenallee 9, 24340, Windeby, Germany
| | - Benjamin Stich
- Institute of Quantitative Genetics and Genomics of Plants (QGGP), Heinrich-Heine-University, Universitätsstraße 1, 40225, Düsseldorf, Germany.
- Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich-Heine-University, Universitätsstraße 1, 40225, Düsseldorf, Germany.
- Julius Kühn-Institut (JKI), Institute for Breeding Research on Agricultural Crops, Rudolf-Schick-Platz 3a, 18190, Sanitz, Germany.
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5
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Raza A, Chen H, Zhang C, Zhuang Y, Sharif Y, Cai T, Yang Q, Soni P, Pandey MK, Varshney RK, Zhuang W. Designing future peanut: the power of genomics-assisted breeding. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:66. [PMID: 38438591 DOI: 10.1007/s00122-024-04575-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 02/03/2024] [Indexed: 03/06/2024]
Abstract
KEY MESSAGE Integrating GAB methods with high-throughput phenotyping, genome editing, and speed breeding hold great potential in designing future smart peanut cultivars to meet market and food supply demands. Cultivated peanut (Arachis hypogaea L.), a legume crop greatly valued for its nourishing food, cooking oil, and fodder, is extensively grown worldwide. Despite decades of classical breeding efforts, the actual on-farm yield of peanut remains below its potential productivity due to the complicated interplay of genotype, environment, and management factors, as well as their intricate interactions. Integrating modern genomics tools into crop breeding is necessary to fast-track breeding efficiency and rapid progress. When combined with speed breeding methods, this integration can substantially accelerate the breeding process, leading to faster access of improved varieties to farmers. Availability of high-quality reference genomes for wild diploid progenitors and cultivated peanuts has accelerated the process of gene/quantitative locus discovery, developing markers and genotyping assays as well as a few molecular breeding products with improved resistance and oil quality. The use of new breeding tools, e.g., genomic selection, haplotype-based breeding, speed breeding, high-throughput phenotyping, and genome editing, is probable to boost genetic gains in peanut. Moreover, renewed attention to efficient selection and exploitation of targeted genetic resources is also needed to design high-quality and high-yielding peanut cultivars with main adaptation attributes. In this context, the combination of genomics-assisted breeding (GAB), genome editing, and speed breeding hold great potential in designing future improved peanut cultivars to meet market and food supply demands.
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Affiliation(s)
- Ali Raza
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Hua Chen
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Chong Zhang
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Yuhui Zhuang
- College of Life Science, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Yasir Sharif
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Tiecheng Cai
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Qiang Yang
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China
| | - Pooja Soni
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, 502324, India
| | - Manish K Pandey
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, 502324, India
| | - Rajeev K Varshney
- WA State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, WA, 6150, Australia.
| | - Weijian Zhuang
- Key Laboratory of Ministry of Education for Genetics, Center of Legume Crop Genetics and Systems Biology, Oil Crops Research Institute, Fujian Agriculture and Forestry University (FAFU), Fuzhou, 350002, China.
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6
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Sato H, Mizoi J, Shinozaki K, Yamaguchi-Shinozaki K. Complex plant responses to drought and heat stress under climate change. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 117:1873-1892. [PMID: 38168757 DOI: 10.1111/tpj.16612] [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: 09/28/2023] [Revised: 12/10/2023] [Accepted: 12/15/2023] [Indexed: 01/05/2024]
Abstract
Global climate change is predicted to result in increased yield losses of agricultural crops caused by environmental conditions. In particular, heat and drought stress are major factors that negatively affect plant development and reproduction, and previous studies have revealed how these stresses induce plant responses at physiological and molecular levels. Here, we provide a comprehensive overview of current knowledge concerning how drought, heat, and combinations of these stress conditions affect the status of plants, including crops, by affecting factors such as stomatal conductance, photosynthetic activity, cellular oxidative conditions, metabolomic profiles, and molecular signaling mechanisms. We further discuss stress-responsive regulatory factors such as transcription factors and signaling factors, which play critical roles in adaptation to both drought and heat stress conditions and potentially function as 'hubs' in drought and/or heat stress responses. Additionally, we present recent findings based on forward genetic approaches that reveal natural variations in agricultural crops that play critical roles in agricultural traits under drought and/or heat conditions. Finally, we provide an overview of the application of decades of study results to actual agricultural fields as a strategy to increase drought and/or heat stress tolerance. This review summarizes our current understanding of plant responses to drought, heat, and combinations of these stress conditions.
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Affiliation(s)
- Hikaru Sato
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8562, Japan
| | - Junya Mizoi
- Department of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan
| | - Kazuo Shinozaki
- RIKEN Center for Sustainable Resource Science, 1-7-22 Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
- Institute for Advanced Research, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan
| | - Kazuko Yamaguchi-Shinozaki
- Department of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan
- Research Institute for Agricultural and Life Sciences, Tokyo University of Agriculture, 1-1-1 Sakuraoka, Setagara-ku, Tokyo, 156-8502, Japan
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Izquierdo P, Sadohara R, Wiesinger J, Glahn R, Urrea C, Cichy K. Genome-wide association and genomic prediction for iron and zinc concentration and iron bioavailability in a collection of yellow dry beans. Front Genet 2024; 15:1330361. [PMID: 38380426 PMCID: PMC10876999 DOI: 10.3389/fgene.2024.1330361] [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: 10/30/2023] [Accepted: 01/03/2024] [Indexed: 02/22/2024] Open
Abstract
Dry bean is a nutrient-dense food targeted in biofortification programs to increase seed iron and zinc levels. The underlying assumption of breeding for higher mineral content is that enhanced iron and zinc levels will deliver health benefits to the consumers of these biofortified foods. This study characterized a diversity panel of 275 genotypes comprising the Yellow Bean Collection (YBC) for seed Fe and Zn concentration, Fe bioavailability (FeBio), and seed yield across 2 years in two field locations. The genetic architecture of each trait was elucidated via genome-wide association studies (GWAS) and the efficacy of genomic prediction (GP) was assessed. Moreover, 82 yellow breeding lines were evaluated for seed Fe and Zn concentrations as well as seed yield, serving as a prediction set for GP models. Large phenotypic variability was identified in all traits evaluated, and variations of up to 2.8 and 13.7-fold were observed for Fe concentration and FeBio, respectively. Prediction accuracies in the YBC ranged from a low of 0.12 for Fe concentration, to a high of 0.72 for FeBio, and an accuracy improvement of 0.03 was observed when a QTN, identified through GWAS, was used as a fixed effect for FeBio. This study provides evidence of the lack of correlation between FeBio estimated in vitro and Fe concentration and highlights the potential of GP in accurately predicting FeBio in yellow beans, offering a cost-effective alternative to the traditional assessment of using Caco2 cell methodologies.
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Affiliation(s)
- Paulo Izquierdo
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, United States
| | - Rie Sadohara
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, United States
| | - Jason Wiesinger
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, United States
| | - Raymond Glahn
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, United States
| | - Carlos Urrea
- Department of Agronomy and Horticulture, Panhandle Research and Extension Center, University of Nebraska-Lincoln, Scottsbluff, NE, United States
| | - Karen Cichy
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, United States
- USDA-ARS, Sugarbeet and Bean Research Unit, East Lansing, MI, United States
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Liu M, Zhang Y, Shaw RK, Zhang X, Li J, Li L, Li S, Adnan M, Jiang F, Bi Y, Yin X, Fan X. Genome-Wide Association Study and Prediction of Tassel Weight of Tropical Maize Germplasm in Multi-Parent Population. Int J Mol Sci 2024; 25:1756. [PMID: 38339032 PMCID: PMC10855296 DOI: 10.3390/ijms25031756] [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: 12/26/2023] [Revised: 01/20/2024] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
Tassel weight (TW) is a crucial agronomic trait that significantly affects pollen supply and grain yield development in maize breeding. To improve maize yield and develop new varieties, a comprehensive understanding of the genetic mechanisms underlying tassel weight is essential. In this study, tropical maize inbred lines, namely CML312, CML373, CML444, and YML46, were selected as female parents and crossed with the elite maize inbred line Ye107, which served as the common male parent, to develop a multi-parent population comprising four F8 recombinant inbred line (RIL) subpopulations. Using 6616 high-quality single nucleotide polymorphism (SNP) markers, we conducted genome-wide association analysis (GWAS) and genomic selection (GS) on 642 F8 RILs in four subpopulations across three different environments. Through GWAS, we identified 16 SNPs that were significantly associated with TW, encompassing two stable loci expressed across multiple environments. Furthermore, within the candidate regions of these SNPs, we discovered four novel candidate genes related to TW, namely Zm00001d044362, Zm00001d011048, Zm00001d011049, and Zm00001d031173 distributed on chromosomes 1, 3, and 8, which have not been previously reported. These genes are involved in processes such as signal transduction, growth and development, protein splicing, and pollen development, all of which play crucial roles in inflorescence meristem development, directly affecting TW. The co-localized SNP, S8_137379725, on chromosome 8 was situated within a 16.569 kb long terminal repeat retrotransposon (LTR-RT), located 22.819 kb upstream and 26.428 kb downstream of the candidate genes (Zm00001d011048 and Zm00001d011049). When comparing three distinct GS models, the BayesB model demonstrated the highest accuracy in predicting TW. This study establishes the theoretical foundation for future research into the genetic mechanisms underlying maize TW and the efficient breeding of high-yielding varieties with desired tassel weight through GS.
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Affiliation(s)
- Meichen Liu
- School of Agriculture, Yunnan University, Kunming 650500, China; (M.L.); (X.Z.); (J.L.); (L.L.); (S.L.)
| | - Yudong Zhang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
| | - Ranjan K. Shaw
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
| | - Xingjie Zhang
- School of Agriculture, Yunnan University, Kunming 650500, China; (M.L.); (X.Z.); (J.L.); (L.L.); (S.L.)
| | - Jinfeng Li
- School of Agriculture, Yunnan University, Kunming 650500, China; (M.L.); (X.Z.); (J.L.); (L.L.); (S.L.)
| | - Linzhuo Li
- School of Agriculture, Yunnan University, Kunming 650500, China; (M.L.); (X.Z.); (J.L.); (L.L.); (S.L.)
| | - Shaoxiong Li
- School of Agriculture, Yunnan University, Kunming 650500, China; (M.L.); (X.Z.); (J.L.); (L.L.); (S.L.)
| | - Muhammad Adnan
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
| | - Fuyan Jiang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
| | - Yaqi Bi
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
| | - Xingfu Yin
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
| | - Xingming Fan
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
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Khanna A, Anumalla M, Ramos J, Cruz MTS, Catolos M, Sajise AG, Gregorio G, Dixit S, Ali J, Islam MR, Singh VK, Rahman MA, Khatun H, Pisano DJ, Bhosale S, Hussain W. Genetic gains in IRRI's rice salinity breeding and elite panel development as a future breeding resource. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:37. [PMID: 38294550 PMCID: PMC10830834 DOI: 10.1007/s00122-024-04545-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 01/05/2024] [Indexed: 02/01/2024]
Abstract
KEY MESSAGE Estimating genetic gains and formulating a future salinity elite breeding panel for rice pave the way for developing better high-yielding salinity tolerant lines with enhanced genetic gains. Genetic gain is a crucial parameter to check the breeding program's success and help optimize future breeding strategies for enhanced genetic gains. To estimate the genetic gains in IRRI's salinity breeding program and identify the best genotypes based on high breeding values for grain yield (kg/ha), we analyzed the historical data from the trials conducted in the IRRI, Philippines and Bangladesh. A two-stage mixed-model approach accounting for experimental design factors and a relationship matrix was fitted to obtain the breeding values for grain yield and estimate genetic trends. A positive genetic trend of 0.1% per annum with a yield advantage of 1.52 kg/ha was observed in IRRI, Philippines. In Bangladesh, we observed a genetic gain of 0.31% per annum with a yield advantage of 14.02 kg/ha. In the released varieties, we observed a genetic gain of 0.12% per annum with a 2.2 kg/ha/year yield advantage in the IRRI, Philippines. For the Bangladesh dataset, a genetic gain of 0.14% per annum with a yield advantage of 5.9 kg/ha/year was observed in the released varieties. Based on breeding values for grain yield, a core set of the top 145 genotypes with higher breeding values of > 2400 kg/ha in the IRRI, Philippines, and > 3500 kg/ha in Bangladesh with a reliability of > 0.4 were selected to develop the elite breeding panel. Conclusively, a recurrent selection breeding strategy integrated with novel technologies like genomic selection and speed breeding is highly required to achieve higher genetic gains in IRRI's salinity breeding programs.
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Affiliation(s)
- Apurva Khanna
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Mahender Anumalla
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Joie Ramos
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Ma Teresa Sta Cruz
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Margaret Catolos
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Andres Godwin Sajise
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Glenn Gregorio
- Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA) and University of Philippines, 4031, Los Baños, Laguna, Philippines
| | - Shalabh Dixit
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Jauhar Ali
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Md Rafiqul Islam
- IRRI South Asia Regional Center (IRRI-SA Hub), Hyderabad, Telangana, 502324, India
| | - Vikas Kumar Singh
- IRRI South Asia Regional Center (IRRI-SA Hub), Hyderabad, Telangana, 502324, India
| | - Md Akhlasur Rahman
- Plant Breeding Division, Bangladesh Rice Research Institute (BRRI), Gazipur, 1701, Bangladesh
| | - Hasina Khatun
- Plant Breeding Division, Bangladesh Rice Research Institute (BRRI), Gazipur, 1701, Bangladesh
| | - Daniel Joseph Pisano
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Sankalp Bhosale
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Waseem Hussain
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines.
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10
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Kang HI, Kim IS, Shim D, Kang KS, Cheon KS. Genomic selection for growth characteristics in Korean red pine ( Pinus densiflora Seibold & Zucc.). FRONTIERS IN PLANT SCIENCE 2024; 15:1285094. [PMID: 38322820 PMCID: PMC10844423 DOI: 10.3389/fpls.2024.1285094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 01/05/2024] [Indexed: 02/08/2024]
Abstract
Traditionally, selective breeding has been used to improve tree growth. However, traditional selection methods are time-consuming and limit annual genetic gain. Genomic selection (GS) offers an alternative to progeny testing by estimating the genotype-based breeding values of individuals based on genomic information using molecular markers. In the present study, we introduced GS to an open-pollinated breeding population of Korean red pine (Pinus densiflora), which is in high demand in South Korea, to shorten the breeding cycle. We compared the prediction accuracies of GS for growth characteristics (diameter at breast height [DBH], height, straightness, and volume) in Korean red pines under various conditions (marker set, model, and training set) and evaluated the selection efficiency of GS compared to traditional selection methods. Training the GS model to include individuals from various environments using genomic best linear unbiased prediction (GBLUP) and markers with a minor allele frequency larger than 0.05 was effective. The optimized model had an accuracy of 0.164-0.498 and a predictive ability of 0.018-0.441. The predictive ability of GBLUP against that of additive best linear unbiased prediction (ABLUP) was 0.86-5.10, and against the square root of heritability was 0.19-0.76, indicating that GS for Korean red pine was as efficient as in previous studies on forest trees. Moreover, the response to GS was higher than that to traditional selection regarding the annual genetic gain. Therefore, we conclude that the trained GS model is more effective than the traditional breeding methods for Korean red pines. We anticipate that the next generation of trees selected by GS will lay the foundation for the accelerated breeding of Korean red pine.
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Affiliation(s)
- Hye-In Kang
- Division of Tree Improvement and Biotechnology, Department of Forest Bio-resources, National Institute of Forest Science, Suwon, Republic of Korea
| | - In Sik Kim
- Division of Tree Improvement and Biotechnology, Department of Forest Bio-resources, National Institute of Forest Science, Suwon, Republic of Korea
| | - Donghwan Shim
- Department of Biological Sciences, Chungnam National University, Daejeon, Republic of Korea
| | - Kyu-Suk Kang
- Department of Agriculture, Forestry and Bioresources, College of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea
| | - Kyeong-Seong Cheon
- Division of Tree Improvement and Biotechnology, Department of Forest Bio-resources, National Institute of Forest Science, Suwon, Republic of Korea
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11
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Freitas LA, Savegnago RP, Alves AAC, Stafuzza NB, Pedrosa VB, Rocha RA, Rosa GJM, Paz CCP. Genome-enabled prediction of indicator traits of resistance to gastrointestinal nematodes in sheep using parametric models and artificial neural networks. Res Vet Sci 2024; 166:105099. [PMID: 38091815 DOI: 10.1016/j.rvsc.2023.105099] [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: 07/12/2023] [Revised: 11/15/2023] [Accepted: 11/19/2023] [Indexed: 01/01/2024]
Abstract
This study aimed to assess the predictive ability of parametric models and artificial neural network method for genomic prediction of the following indicator traits of resistance to gastrointestinal nematodes in Santa Inês sheep: packed cell volume (PCV), fecal egg count (FEC), and Famacha© method (FAM). After quality control, the number of genotyped animals was 551 (PCV), 548 (FEC), and 565 (FAM), and 41,676 SNP. The average prediction accuracy (ACC) calculated by Pearson correlation between observed and predicted values and mean squared errors (MSE) were obtained using genomic best unbiased linear predictor (GBLUP), BayesA, BayesB, Bayesian least absolute shrinkage and selection operator (BLASSO), and Bayesian regularized artificial neural network (three and four hidden neurons, BRANN_3 and BRANN_4, respectively) in a 5-fold cross-validation technique. The average ACC varied from moderate to high according to the trait and models, ranging between 0.418 and 0.546 (PCV), between 0.646 and 0.793 (FEC), and between 0.414 and 0.519 (FAM). Parametric models presented nearly the same ACC and MSE for the studied traits and provided better accuracies than BRANN. The GBLUP, BayesA, BayesB and BLASSO models provided better accuracies than the BRANN_3 method, increasing by around 23% for PCV, and 18.5% for FEC. In conclusion, parametric models are suitable for genome-enabled prediction of indicator traits of resistance to gastrointestinal nematodes in sheep. Due to the small differences in accuracy found between them, the use of the GBLUP model is recommended due to its lower computational costs.
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Affiliation(s)
- L A Freitas
- University of Sao Paulo, Department of Genetics, Ribeirão Preto, São Paulo 14049-900, Brazil; University of Wisconsin, Department of Animal and Dairy Sciences, Madison 53706, USA.
| | - R P Savegnago
- Michigan State University, Department of Animal Science, MI 48864, USA.
| | - A A C Alves
- University of Wisconsin, Department of Animal and Dairy Sciences, Madison 53706, USA.
| | - N B Stafuzza
- Sustainable Livestock Research Center, Animal Science Institute, São José do Rio Preto, São Paulo 15130-000, Brazil
| | - V B Pedrosa
- State University of Ponta Grossa, Ponta Grossa, Paraná 84030-900, Brazil.
| | - R A Rocha
- State University of Ponta Grossa, Ponta Grossa, Paraná 84030-900, Brazil.
| | - G J M Rosa
- University of Wisconsin, Department of Animal and Dairy Sciences, Madison 53706, USA.
| | - C C P Paz
- University of Sao Paulo, Department of Genetics, Ribeirão Preto, São Paulo 14049-900, Brazil; Sustainable Livestock Research Center, Animal Science Institute, São José do Rio Preto, São Paulo 15130-000, Brazil.
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12
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Lamb HJ, Nguyen LT, Copley JP, Engle BN, Hayes BJ, Ross EM. Imputation strategies for genomic prediction using nanopore sequencing. BMC Biol 2023; 21:286. [PMID: 38066581 PMCID: PMC10709982 DOI: 10.1186/s12915-023-01782-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 11/27/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Genomic prediction describes the use of SNP genotypes to predict complex traits and has been widely applied in humans and agricultural species. Genotyping-by-sequencing, a method which uses low-coverage sequence data paired with genotype imputation, is becoming an increasingly popular SNP genotyping method for genomic prediction. The development of Oxford Nanopore Technologies' (ONT) MinION sequencer has now made genotyping-by-sequencing portable and rapid. Here we evaluate the speed and accuracy of genomic predictions using low-coverage ONT sequence data in a population of cattle using four imputation approaches. We also investigate the effect of SNP reference panel size on imputation performance. RESULTS SNP array genotypes and ONT sequence data for 62 beef heifers were used to calculate genomic estimated breeding values (GEBVs) from 641 k SNP for four traits. GEBV accuracy was much higher when genome-wide flanking SNP from sequence data were used to help impute the 641 k panel used for genomic predictions. Using the imputation package QUILT, correlations between ONT and low-density SNP array genomic breeding values were greater than 0.91 and up to 0.97 for sequencing coverages as low as 0.1 × using a reference panel of 48 million SNP. Imputation time was significantly reduced by decreasing the number of flanking sequence SNP used in imputation for all methods. When compared to high-density SNP arrays, genotyping accuracy and genomic breeding value correlations at 0.5 × coverage were also found to be higher than those imputed from low-density arrays. CONCLUSIONS Here we demonstrated accurate genomic prediction is possible with ONT sequence data from sequencing coverages as low as 0.1 × , and imputation time can be as short as 10 min per sample. We also demonstrate that in this population, genotyping-by-sequencing at 0.1 × coverage can be more accurate than imputation from low-density SNP arrays.
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Affiliation(s)
- H J Lamb
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, 4067, Australia.
| | - L T Nguyen
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, 4067, Australia
| | - J P Copley
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, 4067, Australia
| | - B N Engle
- USDA, ARS, U.S. Meat Animal Research Centre, Clay Centre, NE, 68933, USA
| | - B J Hayes
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, 4067, Australia
| | - E M Ross
- Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St. Lucia, QLD, 4067, Australia
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13
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Cyplik A, Piaskowska D, Czembor P, Bocianowski J. The use of weighted multiple linear regression to estimate QTL × QTL × QTL interaction effects of winter wheat (Triticum aestivum L.) doubled-haploid lines. J Appl Genet 2023; 64:679-693. [PMID: 37878169 PMCID: PMC10632291 DOI: 10.1007/s13353-023-00795-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/25/2023] [Accepted: 10/04/2023] [Indexed: 10/26/2023]
Abstract
Knowledge of the magnitude of gene effects and their interactions, their nature, and contribution to determining quantitative traits is very important in conducting an effective breeding program. In traditional breeding, information on the parameter related to additive gene effect and additive-additive interaction (epistasis) and higher-order additive interactions would be useful. Although commonly overlooked in studies, higher-order interactions have a significant impact on phenotypic traits. Failure to account for the effect of triplet interactions in quantitative genetics can significantly underestimate additive QTL effects. Understanding the genetic architecture of quantitative traits is a major challenge in the post-genomic era, especially for quantitative trait locus (QTL) effects, QTL-QTL interactions, and QTL-QTL-QTL interactions. This paper proposes using weighted multiple linear regression to estimate the effects of triple interaction (additive-additive-additive) quantitative trait loci (QTL-QTL-QTL). The material for the study consisted of 126 doubled haploid lines of winter wheat (Mandub × Begra cross). The lines were analyzed for 18 traits, including percentage of necrosis leaf area, percentage of leaf area covered by pycnidia, heading data, and height. The number of genes (the number of effective factors) was lower than the number of QTLs for nine traits, higher for four traits and equal for five traits. The number of triples for unweighted regression ranged from 0 to 9, while for weighted regression, it ranged from 0 to 13. The total aaagu effect ranged from - 14.74 to 15.61, while aaagw ranged from - 23.39 to 21.65. The number of detected threes using weighted regression was higher for two traits and lower for four traits. Forty-nine statistically significant threes of the additive-by-additive-by-additive interaction effects were observed. The QTL most frequently occurring in threes was 4407404 (9 times). The use of weighted regression improved (in absolute value) the assessment of QTL-QTL-QTL interaction effects compared to the assessment based on unweighted regression. The coefficients of determination for the weighted regression model were higher, ranging from 0.8 to 15.5%, than for the unweighted regression. Based on the results, it can be concluded that the QTL-QTL-QTL triple interaction had a significant effect on the expression of quantitative traits. The use of weighted multiple linear regression proved to be a useful statistical tool for estimating additive-additive-additive (aaa) interaction effects. The weighted regression also provided results closer to phenotypic evaluations than estimator values obtained using unweighted regression, which is closer to the true values.
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Affiliation(s)
- Adrian Cyplik
- Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, 60-637, Poznań, Poland
| | - Dominika Piaskowska
- Plant Breeding and Acclimatization Institute - National Research Institute, Department of Applied Biology, Radzików, 05-870, Błonie, Poland
| | - Paweł Czembor
- Plant Breeding and Acclimatization Institute - National Research Institute, Department of Applied Biology, Radzików, 05-870, Błonie, Poland
| | - Jan Bocianowski
- Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, 60-637, Poznań, Poland.
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Heinrich F, Lange TM, Kircher M, Ramzan F, Schmitt AO, Gültas M. Exploring the potential of incremental feature selection to improve genomic prediction accuracy. Genet Sel Evol 2023; 55:78. [PMID: 37946104 PMCID: PMC10634161 DOI: 10.1186/s12711-023-00853-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 11/02/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND The ever-increasing availability of high-density genomic markers in the form of single nucleotide polymorphisms (SNPs) enables genomic prediction, i.e. the inference of phenotypes based solely on genomic data, in the field of animal and plant breeding, where it has become an important tool. However, given the limited number of individuals, the abundance of variables (SNPs) can reduce the accuracy of prediction models due to overfitting or irrelevant SNPs. Feature selection can help to reduce the number of irrelevant SNPs and increase the model performance. In this study, we investigated an incremental feature selection approach based on ranking the SNPs according to the results of a genome-wide association study that we combined with random forest as a prediction model, and we applied it on several animal and plant datasets. RESULTS Applying our approach to different datasets yielded a wide range of outcomes, i.e. from a substantial increase in prediction accuracy in a few cases to minor improvements when only a fraction of the available SNPs were used. Compared with models using all available SNPs, our approach was able to achieve comparable performances with a considerably reduced number of SNPs in several cases. Our approach showcased state-of-the-art efficiency and performance while having a faster computation time. CONCLUSIONS The results of our study suggest that our incremental feature selection approach has the potential to improve prediction accuracy substantially. However, this gain seems to depend on the genomic data used. Even for datasets where the number of markers is smaller than the number of individuals, feature selection may still increase the performance of the genomic prediction. Our approach is implemented in R and is available at https://github.com/FelixHeinrich/GP_with_IFS/ .
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Affiliation(s)
- Felix Heinrich
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075, Göttingen, Germany.
| | - Thomas Martin Lange
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075, Göttingen, Germany
| | - Magdalena Kircher
- Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Bünteweg 17p, 30559, Hannover, Germany
| | - Faisal Ramzan
- Institute of Animal and Dairy Sciences, University of Agriculture Faisalabad, Jail Road, 38000, Faisalabad, Pakistan
| | - Armin Otto Schmitt
- Breeding Informatics Group, Department of Animal Sciences, Georg-August University, Margarethe von Wrangell-Weg 7, 37075, Göttingen, Germany
- Center for Integrated Breeding Research (CiBreed), Georg-August University, Albrecht-Thaer-Weg 3, 37075, Göttingen, Germany
| | - Mehmet Gültas
- Center for Integrated Breeding Research (CiBreed), Georg-August University, Albrecht-Thaer-Weg 3, 37075, Göttingen, Germany.
- Faculty of Agriculture, South Westphalia University of Applied Sciences, 59494, Soest, Germany.
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15
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Ahmed SF, Ahmed JU, Hasan M, Mohi-Ud-Din M. Assessment of genetic variation among wheat genotypes for drought tolerance utilizing microsatellite markers and morpho-physiological characteristics. Heliyon 2023; 9:e21629. [PMID: 38027610 PMCID: PMC10658252 DOI: 10.1016/j.heliyon.2023.e21629] [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: 08/05/2023] [Revised: 09/18/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Drought is a major abiotic stress that severely limits sustainable wheat (Triticum aestivum L.) productivity via morphological and physio-biochemical alterations of cellular processes. The complex nature and polygenic control of drought tolerance traits make breeding tolerant genotypes quite challenging. However, naturally occurring variabilities among wheat germplasm resources could potentially help combating drought. The present study was conducted to assess the drought tolerance of 18 Bangladeshi hexaploid wheat genotypes, focusing on the identification of potent sources of diversity by combining microsatellite markers, also known as single sequence repeat markers, and morpho-physiological characteristics that might help accelerating wheat crop improvement programs. Initially, the genotypes were evaluated using 25 microsatellite markers followed by an on-field evaluation of 7 morphological traits (plant height, spike number, spike length, grains per spike, 1000-grain weight, grain yield, biological yield) and 6 physiological traits (SPAD value, membrane stability index, leaf relative water content, proline content, canopy temperature depression, and leaf K+ ion content). The field-trial was conducted in a factorial fashion of 18 wheat genotypes and two water regimes (control and drought) following a split-plot randomized complete block design. Regardless of genotype, drought was significantly damaging for all the tested traits; however, substantial variability in drought stress tolerance was evident among the genotypes. Spike length, 1000-grain weight, SPAD value, leaf relative water content, canopy temperature depression, proline content, and potassium (K+) ion content were the most representative of drought-induced growth and yield impairments and also correlated well with the contrasting ability of genotypic tolerance. Microsatellite markers amplified 244 alleles exhibiting 79% genetic diversity. Out of 25 markers, 23 was highly polymorphic showing 77% average polymorphism. Morpho-physiological trait-based hierarchical clustering and microsatellite marker-based neighbor-jointing clustering both revealed three genotypic clusters with 71% co-linearity between them. In both cases, the genotypes Kanchan, BAW-1147, BINA Gom 1, BARI Gom 22, BARI Gom 26, and BARI Gom 33 were found to be comparatively more tolerant than the other tested genotypes, showing potential for cultivation in water-deficit environments. The findings of this study would contribute to the present understanding of drought tolerance in wheat and would provide a basis for future genotype selection for drought-tolerant wheat breeding programs.
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Affiliation(s)
- Sheikh Faruk Ahmed
- Department of Crop Botany, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur, 1706, Bangladesh
| | - Jalal Uddin Ahmed
- Department of Crop Botany, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur, 1706, Bangladesh
| | - Mehfuz Hasan
- Department of Genetics and Plant Breeding, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur, 1706, Bangladesh
| | - Mohammed Mohi-Ud-Din
- Department of Crop Botany, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur, 1706, Bangladesh
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16
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Hafeez A, Ali B, Javed MA, Saleem A, Fatima M, Fathi A, Afridi MS, Aydin V, Oral MA, Soudy FA. Plant breeding for harmony between sustainable agriculture, the environment, and global food security: an era of genomics-assisted breeding. PLANTA 2023; 258:97. [PMID: 37823963 DOI: 10.1007/s00425-023-04252-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 09/22/2023] [Indexed: 10/13/2023]
Abstract
MAIN CONCLUSION Genomics-assisted breeding represents a crucial frontier in enhancing the balance between sustainable agriculture, environmental preservation, and global food security. Its precision and efficiency hold the promise of developing resilient crops, reducing resource utilization, and safeguarding biodiversity, ultimately fostering a more sustainable and secure food production system. Agriculture has been seriously threatened over the last 40 years by climate changes that menace global nutrition and food security. Changes in environmental factors like drought, salt concentration, heavy rainfalls, and extremely low or high temperatures can have a detrimental effects on plant development, growth, and yield. Extreme poverty and increasing food demand necessitate the need to break the existing production barriers in several crops. The first decade of twenty-first century marks the rapid development in the discovery of new plant breeding technologies. In contrast, in the second decade, the focus turned to extracting information from massive genomic frameworks, speculating gene-to-phenotype associations, and producing resilient crops. In this review, we will encompass the causes, effects of abiotic stresses and how they can be addressed using plant breeding technologies. Both conventional and modern breeding technologies will be highlighted. Moreover, the challenges like the commercialization of biotechnological products faced by proponents and developers will also be accentuated. The crux of this review is to mention the available breeding technologies that can deliver crops with high nutrition and climate resilience for sustainable agriculture.
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Affiliation(s)
- Aqsa Hafeez
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan
| | - Baber Ali
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan.
| | - Muhammad Ammar Javed
- Institute of Industrial Biotechnology, Government College University, Lahore, 54000, Pakistan
| | - Aroona Saleem
- Institute of Industrial Biotechnology, Government College University, Lahore, 54000, Pakistan
| | - Mahreen Fatima
- Faculty of Biosciences, Cholistan University of Veterinary and Animal Sciences, Bahawalpur, 63100, Pakistan
| | - Amin Fathi
- Department of Agronomy, Ayatollah Amoli Branch, Islamic Azad University, Amol, 46151, Iran
| | - Muhammad Siddique Afridi
- Department of Plant Pathology, Federal University of Lavras (UFLA), Lavras, MG, 37200-900, Brazil
| | - Veysel Aydin
- Sason Vocational School, Department of Plant and Animal Production, Batman University, Batman, 72060, Turkey
| | - Mükerrem Atalay Oral
- Elmalı Vocational School of Higher Education, Akdeniz University, Antalya, 07058, Turkey
| | - Fathia A Soudy
- Genetics and Genetic Engineering Department, Faculty of Agriculture, Benha University, Moshtohor, 13736, Egypt
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17
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Liu S, Zenda T, Tian Z, Huang Z. Metabolic pathways engineering for drought or/and heat tolerance in cereals. FRONTIERS IN PLANT SCIENCE 2023; 14:1111875. [PMID: 37810398 PMCID: PMC10557149 DOI: 10.3389/fpls.2023.1111875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 09/04/2023] [Indexed: 10/10/2023]
Abstract
Drought (D) and heat (H) are the two major abiotic stresses hindering cereal crop growth and productivity, either singly or in combination (D/+H), by imposing various negative impacts on plant physiological and biochemical processes. Consequently, this decreases overall cereal crop production and impacts global food availability and human nutrition. To achieve global food and nutrition security vis-a-vis global climate change, deployment of new strategies for enhancing crop D/+H stress tolerance and higher nutritive value in cereals is imperative. This depends on first gaining a mechanistic understanding of the mechanisms underlying D/+H stress response. Meanwhile, functional genomics has revealed several stress-related genes that have been successfully used in target-gene approach to generate stress-tolerant cultivars and sustain crop productivity over the past decades. However, the fast-changing climate, coupled with the complexity and multigenic nature of D/+H tolerance suggest that single-gene/trait targeting may not suffice in improving such traits. Hence, in this review-cum-perspective, we advance that targeted multiple-gene or metabolic pathway manipulation could represent the most effective approach for improving D/+H stress tolerance. First, we highlight the impact of D/+H stress on cereal crops, and the elaborate plant physiological and molecular responses. We then discuss how key primary metabolism- and secondary metabolism-related metabolic pathways, including carbon metabolism, starch metabolism, phenylpropanoid biosynthesis, γ-aminobutyric acid (GABA) biosynthesis, and phytohormone biosynthesis and signaling can be modified using modern molecular biotechnology approaches such as CRISPR-Cas9 system and synthetic biology (Synbio) to enhance D/+H tolerance in cereal crops. Understandably, several bottlenecks hinder metabolic pathway modification, including those related to feedback regulation, gene functional annotation, complex crosstalk between pathways, and metabolomics data and spatiotemporal gene expressions analyses. Nonetheless, recent advances in molecular biotechnology, genome-editing, single-cell metabolomics, and data annotation and analysis approaches, when integrated, offer unprecedented opportunities for pathway engineering for enhancing crop D/+H stress tolerance and improved yield. Especially, Synbio-based strategies will accelerate the development of climate resilient and nutrient-dense cereals, critical for achieving global food security and combating malnutrition.
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Affiliation(s)
- Songtao Liu
- Hebei Key Laboratory of Quality & Safety Analysis-Testing for Agro-Products and Food, Hebei North University, Zhangjiakou, China
| | - Tinashe Zenda
- State Key Laboratory of North China Crop Improvement and Regulation, Hebei Agricultural University, Baoding, China
| | - Zaimin Tian
- Hebei Key Laboratory of Quality & Safety Analysis-Testing for Agro-Products and Food, Hebei North University, Zhangjiakou, China
| | - Zhihong Huang
- Hebei Key Laboratory of Quality & Safety Analysis-Testing for Agro-Products and Food, Hebei North University, Zhangjiakou, China
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18
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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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19
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Roth L, Fossati D, Krähenbühl P, Walter A, Hund A. Image-based phenomic prediction can provide valuable decision support in wheat breeding. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:162. [PMID: 37368140 DOI: 10.1007/s00122-023-04395-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 05/29/2023] [Indexed: 06/28/2023]
Abstract
KEY MESSAGE Genotype-by-environment interactions of secondary traits based on high-throughput field phenotyping are less complex than those of target traits, allowing for a phenomic selection in unreplicated early generation trials. Traditionally, breeders' selection decisions in early generations are largely based on visual observations in the field. With the advent of affordable genome sequencing and high-throughput phenotyping technologies, enhancing breeders' ratings with such information became attractive. In this research, it is hypothesized that G[Formula: see text]E interactions of secondary traits (i.e., growth dynamics' traits) are less complex than those of related target traits (e.g., yield). Thus, phenomic selection (PS) may allow selecting for genotypes with beneficial response-pattern in a defined population of environments. A set of 45 winter wheat varieties was grown at 5 year-sites and analyzed with linear and factor-analytic (FA) mixed models to estimate G[Formula: see text]E interactions of secondary and target traits. The dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose-response curve parameters. Most of these secondary traits and grain protein content showed little G[Formula: see text]E interactions. In contrast, the modeling of G[Formula: see text]E for yield required a FA model with two factors. A trained PS model predicted overall yield performance, yield stability and grain protein content with correlations of 0.43, 0.30 and 0.34. While these accuracies are modest and do not outperform well-trained GS models, PS additionally provided insights into the physiological basis of target traits. An ideotype was identified that potentially avoids the negative pleiotropic effects between yield and protein content.
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Affiliation(s)
- Lukas Roth
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland.
| | | | - Patrick Krähenbühl
- Delley Samen und Pflanzen AG, Route de Portalban 40, 1567, Delley, Switzerland
| | - Achim Walter
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland
| | - Andreas Hund
- Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland
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20
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Cyplik A, Bocianowski J. A Comparison of Methods to Estimate Additive-by-Additive-by-Additive of QTL×QTL×QTL Interaction Effects by Monte Carlo Simulation Studies. Int J Mol Sci 2023; 24:10043. [PMID: 37373191 DOI: 10.3390/ijms241210043] [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: 04/25/2023] [Revised: 06/05/2023] [Accepted: 06/11/2023] [Indexed: 06/29/2023] Open
Abstract
The goal of the breeding process is to obtain new genotypes with traits improved over the parental forms. Parameters related to the additive effect of genes as well as their interactions (such as epistasis of gene-by-gene interaction effect and additive-by-additive-by-additive of gene-by-gene-by-gene interaction effect) can influence decisions on the suitability of breeding material for this purpose. Understanding the genetic architecture of complex traits is a major challenge in the post-genomic era, especially for quantitative trait locus (QTL) effects, QTL-by-QTL interactions and QTL-by-QTL-by-QTL interactions. With regards to the comparing methods for estimating additive-by-additive-by-additive of QTL×QTL×QTL interaction effects by Monte Carlo simulation studies, there are no publications in the open literature. The parameter combinations assumed in the presented simulation studies represented 84 different experimental situations. The use of weighted regression may be the preferred method for estimating additive-by-additive-by-additive of QTL-QTL-QTL triples interaction effects, as it provides results closer to the true values of total additive-by-additive-by-additive interaction effects than using unweighted regression. This is also indicated by the obtained values of the determination coefficients of the proposed models.
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Affiliation(s)
- Adrian Cyplik
- Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, 60-637 Poznań, Poland
| | - Jan Bocianowski
- Department of Mathematical and Statistical Methods, Poznań University of Life Sciences, Wojska Polskiego 28, 60-637 Poznań, Poland
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21
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Miller MJ, Song Q, Fallen B, Li Z. Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean ( Glycine max). FRONTIERS IN PLANT SCIENCE 2023; 14:1171135. [PMID: 37235007 PMCID: PMC10206060 DOI: 10.3389/fpls.2023.1171135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 04/17/2023] [Indexed: 05/28/2023]
Abstract
Improving yield is a primary soybean breeding goal, as yield is the main determinant of soybean's profitability. Within the breeding process, selection of cross combinations is one of most important elements. Cross prediction will assist soybean breeders in identifying the best cross combinations among parental genotypes prior to crossing, increasing genetic gain and breeding efficiency. In this study optimal cross selection methods were created and applied in soybean and validated using historical data from the University of Georgia soybean breeding program, under multiple training set compositions and marker densities utilizing multiple genomic selection models for marker evaluation. Plant materials consisted of 702 advanced breeding lines evaluated in multiple environments and genotyped using SoySNP6k BeadChips. An additional marker set, the SoySNP3k marker set, was tested in this study as well. Optimal cross selection methods were used to predict the yield of 42 previously made crosses and compared to the performance of the cross's offspring in replicated field trials. The best prediction accuracy was obtained when using Extended Genomic BLUP with the SoySNP6k marker set, consisting of 3,762 polymorphic markers, with an accuracy of 0.56 with a training set maximally related to the crosses predicted and 0.4 in a training set with minimized relatedness to predicted crosses. Prediction accuracy was most significantly impacted by training set relatedness to the predicted crosses, marker density, and the genomic model used to predict marker effects. The usefulness criterion selected had an impact on prediction accuracy within training sets with low relatedness to the crosses predicted. Optimal cross prediction provides a useful method that assists plant breeders in selecting crosses in soybean breeding.
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Affiliation(s)
- Mark J. Miller
- Institute of Plant Breeding, Genetics and Genomics, and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, United States
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, United States Department of Agriculture - Agricultural Research Service, Beltsville, MD, United States
| | - Benjamin Fallen
- Soybean and Nitrogen Fixation Research Unit, United States Department of Agriculture - Agricultural Research Service, Raleigh, NC, United States
| | - Zenglu Li
- Institute of Plant Breeding, Genetics and Genomics, and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, United States
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22
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Liang M, Cao S, Deng T, Du L, Li K, An B, Du Y, Xu L, Zhang L, Gao X, Li J, Guo P, Gao H. MAK: a machine learning framework improved genomic prediction via multi-target ensemble regressor chains and automatic selection of assistant traits. Brief Bioinform 2023; 24:7031157. [PMID: 36752363 DOI: 10.1093/bib/bbad043] [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: 10/22/2022] [Revised: 01/13/2023] [Accepted: 01/20/2023] [Indexed: 02/09/2023] Open
Abstract
Incorporating the genotypic and phenotypic of the correlated traits into the multi-trait model can significantly improve the prediction accuracy of the target trait in animal and plant breeding, as well as human genetics. However, in most cases, the phenotypic information of the correlated and target trait of the individual to be evaluated was null simultaneously, particularly for the newborn. Therefore, we propose a machine learning framework, MAK, to improve the prediction accuracy of the target trait by constructing the multi-target ensemble regression chains and selecting the assistant trait automatically, which predicted the genomic estimated breeding values of the target trait using genotypic information only. The prediction ability of MAK was significantly more robust than the genomic best linear unbiased prediction, BayesB, BayesRR and the multi trait Bayesian method in the four real animal and plant datasets, and the computational efficiency of MAK was roughly 100 times faster than BayesB and BayesRR.
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Affiliation(s)
- Mang Liang
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Sheng Cao
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Tianyu Deng
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Lili Du
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Keanning Li
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Bingxing An
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Yueying Du
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Lingyang Xu
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Lupei Zhang
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Xue Gao
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | - Junya Li
- Chinese Academy of Agricultural Sciences Institute of Animal Science
| | | | - Huijiang Gao
- Chinese Academy of Agricultural Sciences Institute of Animal Science
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23
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Sabag I, Bi Y, Peleg Z, Morota G. Multi-environment analysis enhances genomic prediction accuracy of agronomic traits in sesame. Front Genet 2023; 14:1108416. [PMID: 36992702 PMCID: PMC10040590 DOI: 10.3389/fgene.2023.1108416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Accepted: 02/27/2023] [Indexed: 03/16/2023] Open
Abstract
Introduction: Sesame is an ancient oilseed crop containing many valuable nutritional components. The demand for sesame seeds and their products has recently increased worldwide, making it necessary to enhance the development of high-yielding cultivars. One approach to enhance genetic gain in breeding programs is genomic selection. However, studies on genomic selection and genomic prediction in sesame have yet to be conducted.Methods: In this study, we performed genomic prediction for agronomic traits using the phenotypes and genotypes of a sesame diversity panel grown under Mediterranean climatic conditions over two growing seasons. We aimed to assess prediction accuracy for nine important agronomic traits in sesame using single- and multi-environment analyses.Results: In single-environment analysis, genomic best linear unbiased prediction, BayesB, BayesC, and reproducing kernel Hilbert spaces models showed no substantial differences. The average prediction accuracy of the nine traits across these models ranged from 0.39 to 0.79 for both growing seasons. In the multi-environment analysis, the marker-by-environment interaction model, which decomposed the marker effects into components shared across environments and environment-specific deviations, improved the prediction accuracies for all traits by 15%–58% compared to the single-environment model, particularly when borrowing information from other environments was made possible.Discussion: Our results showed that single-environment analysis produced moderate-to-high genomic prediction accuracy for agronomic traits in sesame. The multi-environment analysis further enhanced this accuracy by exploiting marker-by-environment interaction. We concluded that genomic prediction using multi-environmental trial data could improve efforts for breeding cultivars adapted to the semi-arid Mediterranean climate.
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Affiliation(s)
- Idan Sabag
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot, Israel
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
| | - Ye Bi
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
| | - Zvi Peleg
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, Rehovot, Israel
- *Correspondence: Zvi Peleg, ; Gota Morota,
| | - Gota Morota
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
- *Correspondence: Zvi Peleg, ; Gota Morota,
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24
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Cooper M, Powell O, Gho C, Tang T, Messina C. Extending the breeder's equation to take aim at the target population of environments. FRONTIERS IN PLANT SCIENCE 2023; 14:1129591. [PMID: 36895882 PMCID: PMC9990092 DOI: 10.3389/fpls.2023.1129591] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 02/10/2023] [Indexed: 06/18/2023]
Abstract
A major focus for genomic prediction has been on improving trait prediction accuracy using combinations of algorithms and the training data sets available from plant breeding multi-environment trials (METs). Any improvements in prediction accuracy are viewed as pathways to improve traits in the reference population of genotypes and product performance in the target population of environments (TPE). To realize these breeding outcomes there must be a positive MET-TPE relationship that provides consistency between the trait variation expressed within the MET data sets that are used to train the genome-to-phenome (G2P) model for applications of genomic prediction and the realized trait and performance differences in the TPE for the genotypes that are the prediction targets. The strength of this MET-TPE relationship is usually assumed to be high, however it is rarely quantified. To date investigations of genomic prediction methods have focused on improving prediction accuracy within MET training data sets, with less attention to quantifying the structure of the TPE and the MET-TPE relationship and their potential impact on training the G2P model for applications of genomic prediction to accelerate breeding outcomes for the on-farm TPE. We extend the breeder's equation and use an example to demonstrate the importance of the MET-TPE relationship as a key component for the design of genomic prediction methods to realize improved rates of genetic gain for the target yield, quality, stress tolerance and yield stability traits in the on-farm TPE.
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Affiliation(s)
- Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia
- Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, Australia
| | - Owen Powell
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD, Australia
- Australian Research Council Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, Australia
| | - Carla Gho
- School of Agriculture & Food Sciences, The University of Queensland, Brisbane, QLD, Australia
| | - Tom Tang
- Corteva Agriscience, Johnston, IA, United States
| | - Carlos Messina
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
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25
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Guo X, Puritz JB, Wang Z, Proestou D, Allen S, Small J, Verbyla K, Zhao H, Haggard J, Chriss N, Zeng D, Lundgren K, Allam B, Bushek D, Gomez-Chiarri M, Hare M, Hollenbeck C, La Peyre J, Liu M, Lotterhos KE, Plough L, Rawson P, Rikard S, Saillant E, Varney R, Wikfors G, Wilbur A. Development and Evaluation of High-Density SNP Arrays for the Eastern Oyster Crassostrea virginica. MARINE BIOTECHNOLOGY (NEW YORK, N.Y.) 2023; 25:174-191. [PMID: 36622459 DOI: 10.1007/s10126-022-10191-3] [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: 10/12/2022] [Accepted: 12/07/2022] [Indexed: 06/17/2023]
Abstract
The eastern oyster Crassostrea virginica is a major aquaculture species for the USA. The sustainable development of eastern oyster aquaculture depends upon the continued improvement of cultured stocks through advanced breeding technologies. The Eastern Oyster Breeding Consortium (EOBC) was formed to advance the genetics and breeding of the eastern oyster. To facilitate efficient genotyping needed for genomic studies and selection, the consortium developed two single-nucleotide polymorphism (SNP) arrays for the eastern oyster: one screening array with 566K SNPs and one breeders' array with 66K SNPs. The 566K screening array was developed based on whole-genome resequencing data from 292 oysters from Atlantic and Gulf of Mexico populations; it contains 566,262 SNPs including 47K from protein-coding genes with a marker conversion rate of 48.34%. The 66K array was developed using best-performing SNPs from the screening array, which contained 65,893 oyster SNPs including 22,984 genic markers with a calling rate of 99.34%, a concordance rate of 99.81%, and a much-improved marker conversion rate of 92.04%. Null alleles attributable to large indels were found in 13.1% of the SNPs, suggesting that copy number variation is pervasive. Both arrays provided easy identification and separation of selected stocks from wild progenitor populations. The arrays contain 31 mitochondrial SNPs that allowed unambiguous identification of Gulf mitochondrial genotypes in some Atlantic populations. The arrays also contain 756 probes from 13 oyster and human pathogens for possible detection. Our results show that marker conversion rate is low in high polymorphism species and that the two-step process of array development can greatly improve array performance. The two arrays will advance genomic research and accelerate genetic improvement of the eastern oyster by delineating genetic architecture of production traits and enabling genomic selection. The arrays also may be used to monitor pedigree and inbreeding, identify selected stocks and their introgression into wild populations, and assess the success of oyster restoration.
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Affiliation(s)
- Ximing Guo
- Haskin Shellfish Research Laboratory, Rutgers University, 6959 Miller Avenue, Port Norris, NJ, 08349, USA.
| | - Jonathan B Puritz
- Department of Biological Sciences, University of Rhode Island, 120 Flagg Road, Kingston, RI, 02881, USA
| | - Zhenwei Wang
- Haskin Shellfish Research Laboratory, Rutgers University, 6959 Miller Avenue, Port Norris, NJ, 08349, USA
| | - Dina Proestou
- USDA ARS NCWMAC Shellfish Genetics Lab, 120 Flagg Rd., Kingston, RI, 02881, USA
| | - Standish Allen
- Virginia Institute of Marine Science, 1375 Greate Rd., Gloucester Pt., VA, 23062, USA
| | - Jessica Small
- Virginia Institute of Marine Science, 1375 Greate Rd., Gloucester Pt., VA, 23062, USA
| | | | - Honggang Zhao
- Department of Natural Resources and the Environment, Cornell University, Ithaca, NY, 14853, USA
| | - Jaime Haggard
- Haskin Shellfish Research Laboratory, Rutgers University, 6959 Miller Avenue, Port Norris, NJ, 08349, USA
| | - Noah Chriss
- Haskin Shellfish Research Laboratory, Rutgers University, 6959 Miller Avenue, Port Norris, NJ, 08349, USA
| | - Dan Zeng
- Haskin Shellfish Research Laboratory, Rutgers University, 6959 Miller Avenue, Port Norris, NJ, 08349, USA
| | - Kathryn Lundgren
- USDA ARS NCWMAC Shellfish Genetics Lab, 120 Flagg Rd., Kingston, RI, 02881, USA
| | - Bassem Allam
- School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, 11794, USA
| | - David Bushek
- Haskin Shellfish Research Laboratory, Rutgers University, 6959 Miller Avenue, Port Norris, NJ, 08349, USA
| | - Marta Gomez-Chiarri
- Department of Fisheries, Animal and Veterinary Science, University of Rhode Island, 120 Flagg Road, Kingston, RI, 02881, USA
| | - Matthew Hare
- Department of Natural Resources and the Environment, Cornell University, Ithaca, NY, 14853, USA
| | - Christopher Hollenbeck
- Texas A&M University - Corpus Christi, Texas A&M AgriLife Research, 6300 Ocean Drive Unit 5892, Corpus Christi, TX, 78412, USA
| | - Jerome La Peyre
- School of Animal Sciences, Louisiana State University Agricultural Center, 201 Animal and Food Sciences Laboratory Building, Forestry Lane, Baton Rouge, LA, 70803, USA
| | - Ming Liu
- Patuxent Environmental and Aquatic Research Laboratory, Morgan State University, 10545 Mackall Road, Saint Leonard, MD, 20685, USA
| | - Katie E Lotterhos
- Northeastern Marine Science Center, 430 Nahant Rd, Nahant, MA, 01908, USA
| | - Louis Plough
- Horn Point Lab, University of Maryland, 5745 Lovers Lane, Cambridge, MD, 21613, USA
| | - Paul Rawson
- School of Marine Sciences, University of Maine, 5751 Murray Hall, , Orono, ME, 04469, USA
| | - Scott Rikard
- School of Fisheries Aquaculture and Aquatic Sciences, Auburn University Shellfish Laboratory, Auburn University, 150 Agassiz St., Dauphin Island, AL, 36528, USA
| | - Eric Saillant
- School of Ocean Science and Engineering, The University of Southern Mississippi, 103 McIlwain Drive, Ocean Springs, MS, 39564, USA
| | - Robin Varney
- Shellfish Research Hatchery, University of North Carolina Wilmington, 5600 Marvin K. Moss Ln., Wilmington, NC, 28409, USA
| | - Gary Wikfors
- Milford CT Laboratory, NOAA Fisheries, 212 Rogers Avenue, Milford, CT, 06460, USA
| | - Ami Wilbur
- Shellfish Research Hatchery, University of North Carolina Wilmington, 5600 Marvin K. Moss Ln., Wilmington, NC, 28409, USA
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26
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Gevartosky R, Carvalho HF, Costa-Neto G, Montesinos-López OA, Crossa J, Fritsche-Neto R. Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize. BMC PLANT BIOLOGY 2023; 23:10. [PMID: 36604618 PMCID: PMC9814176 DOI: 10.1186/s12870-022-03975-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Success in any genomic prediction platform is directly dependent on establishing a representative training set. This is a complex task, even in single-trait single-environment conditions and tends to be even more intricated wherein additional information from envirotyping and correlated traits are considered. Here, we aimed to design optimized training sets focused on genomic prediction, considering multi-trait multi-environment trials, and how those methods may increase accuracy reducing phenotyping costs. For that, we considered single-trait multi-environment trials and multi-trait multi-environment trials for three traits: grain yield, plant height, and ear height, two datasets, and two cross-validation schemes. Next, two strategies for designing optimized training sets were conceived, first considering only the genomic by environment by trait interaction (GET), while a second including large-scale environmental data (W, enviromics) as genomic by enviromic by trait interaction (GWT). The effective number of individuals (genotypes × environments × traits) was assumed as those that represent at least 98% of each kernel (GET or GWT) variation, in which those individuals were then selected by a genetic algorithm based on prediction error variance criteria to compose an optimized training set for genomic prediction purposes. RESULTS The combined use of genomic and enviromic data efficiently designs optimized training sets for genomic prediction, improving the response to selection per dollar invested by up to 145% when compared to the model without enviromic data, and even more when compared to cross validation scheme with 70% of training set or pure phenotypic selection. Prediction models that include G × E or enviromic data + G × E yielded better prediction ability. CONCLUSIONS Our findings indicate that a genomic by enviromic by trait interaction kernel associated with genetic algorithms is efficient and can be proposed as a promising approach to designing optimized training sets for genomic prediction when the variance-covariance matrix of traits is available. Additionally, great improvements in the genetic gains per dollar invested were observed, suggesting that a good allocation of resources can be deployed by using the proposed approach.
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Affiliation(s)
- Raysa Gevartosky
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil.
| | - Humberto Fanelli Carvalho
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Germano Costa-Neto
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil
- Institute for Genomics Diversity, Cornell University, Ithaca, NY, USA
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera Mexico-Veracruz, CP 52640, Texcoco, Edo. de México, Mexico
- Colegio de Postgraduados, CP 56230, Montecillos, Edo. de México, Mexico
| | - Roberto Fritsche-Neto
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil
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27
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Cooper M, Messina CD. Breeding crops for drought-affected environments and improved climate resilience. THE PLANT CELL 2023; 35:162-186. [PMID: 36370076 PMCID: PMC9806606 DOI: 10.1093/plcell/koac321] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/01/2022] [Indexed: 05/12/2023]
Abstract
Breeding climate-resilient crops with improved levels of abiotic and biotic stress resistance as a response to climate change presents both opportunities and challenges. Applying the framework of the "breeder's equation," which is used to predict the response to selection for a breeding program cycle, we review methodologies and strategies that have been used to successfully breed crops with improved levels of drought resistance, where the target population of environments (TPEs) is a spatially and temporally heterogeneous mixture of drought-affected and favorable (water-sufficient) environments. Long-term improvement of temperate maize for the US corn belt is used as a case study and compared with progress for other crops and geographies. Integration of trait information across scales, from genomes to ecosystems, is needed to accurately predict yield outcomes for genotypes within the current and future TPEs. This will require transdisciplinary teams to explore, identify, and exploit novel opportunities to accelerate breeding program outcomes; both improved germplasm resources and improved products (cultivars, hybrids, clones, and populations) that outperform and replace the products in use by farmers, in combination with modified agronomic management strategies suited to their local environments.
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Affiliation(s)
| | - Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, Florida 32611, USA
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28
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Xiong W, Reynolds M, Xu Y. Climate change challenges plant breeding. CURRENT OPINION IN PLANT BIOLOGY 2022; 70:102308. [PMID: 36279790 DOI: 10.1016/j.pbi.2022.102308] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/12/2022] [Accepted: 09/17/2022] [Indexed: 06/16/2023]
Abstract
Plant breeding is important to cope with climate change impacts, complementing crop management and policy interventions to ensure global food production. However, changes in environmental factors also affect the objectives, efficiency, and genetic gains of the current plant breeding system. In this review, we summarize the challenges prompted by climate change to breeding climate-resilient crops and the limitations of the next-generation breeding approach in addressing climate change. It is anticipated that the integration of multi-disciplines and technologies into three schemes of genotyping, phenotyping, and envirotyping will result in the delivery of climate change-ready crops in less time.
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Affiliation(s)
- Wei Xiong
- CIMMYT-Henan Joint Center for Wheat and Maize Improvement, Henan Agricultural University, Zhengzhou, China; International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco, Mexico.
| | - Matthew Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco, Mexico
| | - Yunbi Xu
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco, Mexico; Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
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29
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Xu Y, Zhang X, Li H, Zheng H, Zhang J, Olsen MS, Varshney RK, Prasanna BM, Qian Q. Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. MOLECULAR PLANT 2022; 15:1664-1695. [PMID: 36081348 DOI: 10.1016/j.molp.2022.09.001] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/20/2022] [Accepted: 09/02/2022] [Indexed: 05/12/2023]
Abstract
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.
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Affiliation(s)
- Yunbi Xu
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; CIMMYT-China Tropical Maize Research Center, School of Food Science and Engineering, Foshan University, Foshan, Guangdong 528231, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China.
| | - Xingping Zhang
- Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China
| | - Huihui Li
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, Hainan 572024, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China
| | - Jianan Zhang
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang, Hebei 050035, China
| | - Michael S Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Rajeev K Varshney
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Australia
| | - Boddupalli M Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Qian Qian
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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Ballén-Taborda C, Lyerly J, Smith J, Howell K, Brown-Guedira G, Babar MA, Harrison SA, Mason RE, Mergoum M, Murphy JP, Sutton R, Griffey CA, Boyles RE. Utilizing genomics and historical data to optimize gene pools for new breeding programs: A case study in winter wheat. Front Genet 2022; 13:964684. [PMID: 36276956 PMCID: PMC9585219 DOI: 10.3389/fgene.2022.964684] [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: 06/08/2022] [Accepted: 08/05/2022] [Indexed: 11/13/2022] Open
Abstract
With the rapid generation and preservation of both genomic and phenotypic information for many genotypes within crops and across locations, emerging breeding programs have a valuable opportunity to leverage these resources to 1) establish the most appropriate genetic foundation at program inception and 2) implement robust genomic prediction platforms that can effectively select future breeding lines. Integrating genomics-enabled1 breeding into cultivar development can save costs and allow resources to be reallocated towards advanced (i.e., later) stages of field evaluation, which can facilitate an increased number of testing locations and replicates within locations. In this context, a reestablished winter wheat breeding program was used as a case study to understand best practices to leverage and tailor existing genomic and phenotypic resources to determine optimal genetics for a specific target population of environments. First, historical multi-environment phenotype data, representing 1,285 advanced breeding lines, were compiled from multi-institutional testing as part of the SunGrains cooperative and used to produce GGE biplots and PCA for yield. Locations were clustered based on highly correlated line performance among the target population of environments into 22 subsets. For each of the subsets generated, EMMs and BLUPs were calculated using linear models with the ‘lme4’ R package. Second, for each subset, TPs representative of the new SC breeding lines were determined based on genetic relatedness using the ‘STPGA’ R package. Third, for each TP, phenotypic values and SNP data were incorporated into the ‘rrBLUP’ mixed models for generation of GEBVs of YLD, TW, HD and PH. Using a five-fold cross-validation strategy, an average accuracy of r = 0.42 was obtained for yield between all TPs. The validation performed with 58 SC elite breeding lines resulted in an accuracy of r = 0.62 when the TP included complete historical data. Lastly, QTL-by-environment interaction for 18 major effect genes across three geographic regions was examined. Lines harboring major QTL in the absence of disease could potentially underperform (e.g., Fhb1 R-gene), whereas it is advantageous to express a major QTL under biotic pressure (e.g., stripe rust R-gene). This study highlights the importance of genomics-enabled breeding and multi-institutional partnerships to accelerate cultivar development.
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Affiliation(s)
- Carolina Ballén-Taborda
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
- Pee Dee Research and Education Center, Clemson University, Florence, SC, United States
| | - Jeanette Lyerly
- Crop and Soil Sciences Department, North Carolina State University, Raleigh, NC, United States
| | - Jared Smith
- U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS), Raleigh, NC, United States
| | - Kimberly Howell
- U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS), Raleigh, NC, United States
| | - Gina Brown-Guedira
- Crop and Soil Sciences Department, North Carolina State University, Raleigh, NC, United States
- U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS), Raleigh, NC, United States
| | - Md. Ali Babar
- Agronomy Department, University of Florida, Gainesville, FL, United States
| | - Stephen A. Harrison
- School of Plant, Environmental and Soil Sciences, Louisiana State University, Baton Rouge, LA, United States
| | - Richard E. Mason
- College of Agricultural Sciences, Colorado State University, Fort Collins, CO, United States
| | - Mohamed Mergoum
- Department of Crop and Soil Sciences, University of Georgia, Griffin, GA, United States
| | - J. Paul Murphy
- Crop and Soil Sciences Department, North Carolina State University, Raleigh, NC, United States
| | - Russell Sutton
- Department of Soil and Crop Sciences, Texas A&M University, Commerce, TX, United States
| | - Carl A. Griffey
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Richard E. Boyles
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
- Pee Dee Research and Education Center, Clemson University, Florence, SC, United States
- *Correspondence: Richard E. Boyles,
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Sabadin F, DoVale JC, Platten JD, Fritsche-Neto R. Optimizing self-pollinated crop breeding employing genomic selection: From schemes to updating training sets. FRONTIERS IN PLANT SCIENCE 2022; 13:935885. [PMID: 36275547 PMCID: PMC9583387 DOI: 10.3389/fpls.2022.935885] [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: 05/04/2022] [Accepted: 09/12/2022] [Indexed: 06/16/2023]
Abstract
Long-term breeding schemes using genomic selection (GS) can boost the response to selection per year. Although several studies have shown that GS delivers a higher response to selection, only a few analyze which stage GS produces better results and how to update the training population to maintain prediction accuracy. We used stochastic simulation to compare five GS breeding schemes in a self-pollinated long-term breeding program. Also, we evaluated four strategies, using distinct methods and sizes, to update the training set. Finally, regarding breeding schemes, we proposed a new approach using GS to select the best individuals in each F2 progeny, based on genomic estimated breeding values and genetic divergence, to cross them and generate a new recombination event. Our results showed that the best scenario was using GS in F2, followed by the phenotypic selection of new parents in F4. For TS updating, adding new data every cycle (over 768) to update the TS maintains the prediction accuracy at satisfactory levels for more breeding cycles. However, only the last three generations can be kept in the TS, optimizing the genetic relationship between TS and the targeted population and reducing the computing demand and risks. Hence, we believe that our results may help breeders optimize GS in their programs and improve genetic gain in long-term schemes.
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Affiliation(s)
- Felipe Sabadin
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Julio César DoVale
- Department of Crop Science, Federal University of Ceará, Fortaleza, Ceará, Brazil
| | | | - Roberto Fritsche-Neto
- International Rice Research Institute (IRRI), Los Baños, Philippines
- H. Rouse Caffey Rice Research Station, Louisiana State University (LSU) AgCenter, Rayne, LA, United States
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Seyum EG, Bille NH, Abtew WG, Munyengwa N, Bell JM, Cros D. Genomic selection in tropical perennial crops and plantation trees: a review. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:58. [PMID: 37313015 PMCID: PMC10248687 DOI: 10.1007/s11032-022-01326-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 09/06/2022] [Indexed: 06/15/2023]
Abstract
To overcome the multiple challenges currently faced by agriculture, such as climate change and soil deterioration, more efficient plant breeding strategies are required. Genomic selection (GS) is crucial for the genetic improvement of quantitative traits, as it can increase selection intensity, shorten the generation interval, and improve selection accuracy for traits that are difficult to phenotype. Tropical perennial crops and plantation trees are of major economic importance and have consequently been the subject of many GS articles. In this review, we discuss the factors that affect GS accuracy (statistical models, linkage disequilibrium, information concerning markers, relatedness between training and target populations, the size of the training population, and trait heritability) and the genetic gain expected in these species. The impact of GS will be particularly strong in tropical perennial crops and plantation trees as they have long breeding cycles and constrained selection intensity. Future GS prospects are also discussed. High-throughput phenotyping will allow constructing of large training populations and implementing of phenomic selection. Optimized modeling is needed for longitudinal traits and multi-environment trials. The use of multi-omics, haploblocks, and structural variants will enable going beyond single-locus genotype data. Innovative statistical approaches, like artificial neural networks, are expected to efficiently handle the increasing amounts of heterogeneous multi-scale data. Targeted recombinations on sites identified from profiles of marker effects have the potential to further increase genetic gain. GS can also aid re-domestication and introgression breeding. Finally, GS consortia will play an important role in making the best of these opportunities. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01326-4.
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Affiliation(s)
- Essubalew Getachew Seyum
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
- Department of Horticulture and Plant Sciences, College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box 307, Jimma, Ethiopia
| | - Ngalle Hermine Bille
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
| | - Wosene Gebreselassie Abtew
- Department of Horticulture and Plant Sciences, College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box 307, Jimma, Ethiopia
| | - Norman Munyengwa
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Joseph Martin Bell
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
| | - David Cros
- CIRAD, UMR AGAP Institut, 34398 Montpellier, France
- UMR AGAP Institut, CIRAD, INRAE, Univ. Montpellier, Institut Agro, 34398 Montpellier, France
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Anilkumar C, Sunitha NC, Devate NB, Ramesh S. Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review. PLANTA 2022; 256:87. [PMID: 36149531 DOI: 10.1007/s00425-022-03996-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 09/11/2022] [Indexed: 06/16/2023]
Abstract
Genomic selection and its importance in crop breeding. Integration of GS with new breeding tools and developing SOP for GS to achieve maximum genetic gain with low cost and time. The success of conventional breeding approaches is not sufficient to meet the demand of a growing population for nutritious food and other plant-based products. Whereas, marker assisted selection (MAS) is not efficient in capturing all the favorable alleles responsible for economic traits in the process of crop improvement. Genomic selection (GS) developed in livestock breeding and then adapted to plant breeding promised to overcome the drawbacks of MAS and significantly improve complicated traits controlled by gene/QTL with small effects. Large-scale deployment of GS in important crops, as well as simulation studies in a variety of contexts, addressed G × E interaction effects and non-additive effects, as well as lowering breeding costs and time. The current study provides a complete overview of genomic selection, its process, and importance in modern plant breeding, along with insights into its application. GS has been implemented in the improvement of complex traits including tolerance to biotic and abiotic stresses. Furthermore, this review hypothesises that using GS in conjunction with other crop improvement platforms accelerates the breeding process to increase genetic gain. The objective of this review is to highlight the development of an appropriate GS model, the global open source network for GS, and trans-disciplinary approaches for effective accelerated crop improvement. The current study focused on the application of data science, including machine learning and deep learning tools, to enhance the accuracy of prediction models. Present study emphasizes on developing plant breeding strategies centered on GS combined with routine conventional breeding principles by developing GS-SOP to achieve enhanced genetic gain.
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Affiliation(s)
- C Anilkumar
- ICAR-National Rice Research Institute, Cuttack, India
| | - N C Sunitha
- University of Agricultural Sciences, Bangalore, India
| | | | - S Ramesh
- University of Agricultural Sciences, Bangalore, India.
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Mollandin F, Gilbert H, Croiseau P, Rau A. Accounting for overlapping annotations in genomic prediction models of complex traits. BMC Bioinformatics 2022; 23:365. [PMID: 36068513 PMCID: PMC9446854 DOI: 10.1186/s12859-022-04914-5] [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: 02/16/2022] [Accepted: 08/25/2022] [Indexed: 11/10/2022] Open
Abstract
Background It is now widespread in livestock and plant breeding to use genotyping data to predict phenotypes with genomic prediction models. In parallel, genomic annotations related to a variety of traits are increasing in number and granularity, providing valuable insight into potentially important positions in the genome. The BayesRC model integrates this prior biological information by factorizing the genome according to disjoint annotation categories, in some cases enabling improved prediction of heritable traits. However, BayesRC is not adapted to cases where markers may have multiple annotations. Results We propose two novel Bayesian approaches to account for multi-annotated markers through a cumulative (BayesRC+) or preferential (BayesRC\documentclass[12pt]{minimal}
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\begin{document}$$\pi$$\end{document}π) model of the contribution of multiple annotation categories. We illustrate their performance on simulated data with various genetic architectures and types of annotations. We also explore their use on data from a backcross population of growing pigs in conjunction with annotations constructed using the PigQTLdb. In both simulated and real data, we observed a modest improvement in prediction quality with our models when used with informative annotations. In addition, our results show that BayesRC+ successfully prioritizes multi-annotated markers according to their posterior variance, while BayesRC\documentclass[12pt]{minimal}
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\begin{document}$$\pi$$\end{document}π provides a useful interpretation of informative annotations for multi-annotated markers. Finally, we explore several strategies for constructing annotations from a public database, highlighting the importance of careful consideration of this step. Conclusion When used with annotations that are relevant to the trait under study, BayesRC\documentclass[12pt]{minimal}
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\begin{document}$$\pi$$\end{document}π and BayesRC+ allow for improved prediction and prioritization of multi-annotated markers, and can provide useful biological insight into the genetic architecture of traits. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04914-5.
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Affiliation(s)
- Fanny Mollandin
- INRAE, AgroParisTech, GABI, Université Paris-Saclay, Allée de Vilvert, 78350, Jouy-en-Josas, France.
| | - Hélène Gilbert
- GenPhySE, INRAE, ENVT, Université de Toulouse, 31320, Castanet Tolosan, France
| | - Pascal Croiseau
- INRAE, AgroParisTech, GABI, Université Paris-Saclay, Allée de Vilvert, 78350, Jouy-en-Josas, France
| | - Andrea Rau
- INRAE, AgroParisTech, GABI, Université Paris-Saclay, Allée de Vilvert, 78350, Jouy-en-Josas, France.,BioEcoAgro Joint Research Unit, INRAE, Université de Liège, Université de Lille, Université de Picardie Jules Verne, 50136, Estrée-Mons, France
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35
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Shan D, Ali M, Shahid M, Arif A, Waheed MQ, Xia X, Trethowan R, Tester M, Poland J, Ogbonnaya FC, Rasheed A, He Z, Li H. Genetic networks underlying salinity tolerance in wheat uncovered with genome-wide analyses and selective sweeps. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:2925-2941. [PMID: 35915266 DOI: 10.1007/s00122-022-04153-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 06/16/2022] [Indexed: 06/15/2023]
Abstract
A genetic framework underpinning salinity tolerance at reproductive stage was revealed by genome-wide SNP markers and major adaptability genes in synthetic-derived wheats, and trait-associated loci were used to predict phenotypes. Using wild relatives of crops to identify genes related to improved productivity and resilience to climate extremes is a prioritized area of crop genetic improvement. High salinity is a widespread crop production constraint, and development of salt-tolerant cultivars is a sustainable solution. We evaluated a panel of 294 wheat accessions comprising synthetic-derived wheat lines (SYN-DERs) and modern bread wheat advanced lines under control and high salinity conditions at two locations. The GWAS analysis revealed a quantitative genetic framework of more than 200 loci with minor effect underlying salinity tolerance at reproductive stage. The significant trait-associated SNPs were used to predict phenotypes using a GBLUP model, and the prediction accuracy (r2) ranged between 0.57 and 0.74. The r2 values for flag leaf weight, days to flowering, biomass, and number of spikes per plant were all above 0.70, validating the phenotypic effects of the loci discovered in this study. Furthermore, the germplasm sets were compared to identify selection sweeps associated with salt tolerance loci in SYN-DERs. Six loci associated with salinity tolerance were found to be differentially selected in the SYN-DERs (12.4 Mb on chromosome (chr)1B, 7.1 Mb on chr2A, 11.2 Mb on chr2D, 200 Mb on chr3D, 600 Mb on chr6B, and 700.9 Mb on chr7B). A total of 228 reported markers and genes, including 17 well-characterized genes, were uncovered using GWAS and EigenGWAS. A linkage disequilibrium (LD) block on chr5A, including the Vrn-A1 gene at 575 Mb and its homeologs on chr5D, were strongly associated with multiple yield-related traits and flowering time under salinity stress conditions. The diversity panel was screened with more than 68 kompetitive allele-specific PCR (KASP) markers of functional genes in wheat, and the pleiotropic effects of superior alleles of Rht-1, TaGASR-A1, and TaCwi-A1 were revealed under salinity stress. To effectively utilize the extensive genetic information obtained from the GWAS analysis, a genetic interaction network was constructed to reveal correlations among the investigated traits. The genetic network data combined with GWAS, selective sweeps, and the functional gene survey provided a quantitative genetic framework for identifying differentially retained loci associated with salinity tolerance in wheat.
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Affiliation(s)
- Danting Shan
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), CIMMYT-China Office, 12 Zhongguancun South Street, Beijing, 100081, China
- Nanfan Research Institute, CAAS, Sanya, 572024, Hainan, China
| | - Mohsin Ali
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), CIMMYT-China Office, 12 Zhongguancun South Street, Beijing, 100081, China
- Nanfan Research Institute, CAAS, Sanya, 572024, Hainan, China
| | - Mohammed Shahid
- International Center for Biosaline Agriculture (ICBA), Al Ruwayyah 2, Academic City, Dubai, UAE
| | - Anjuman Arif
- National Institute of Agriculture and Biology (NIAB), Faisalabad, Pakistan
| | | | - Xianchun Xia
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), CIMMYT-China Office, 12 Zhongguancun South Street, Beijing, 100081, China
| | - Richard Trethowan
- Plant Breeding Institute, School of Life and Environmental Sciences, The University of Sydney, Sydney, 2006, Australia
| | - Mark Tester
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KASUT), Thuwal, 23955-6900, Saudi Arabia
| | - Jesse Poland
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KASUT), Thuwal, 23955-6900, Saudi Arabia
- Kansas State University, Manhattan, KS, USA
| | | | - Awais Rasheed
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), CIMMYT-China Office, 12 Zhongguancun South Street, Beijing, 100081, China.
| | - Zhonghu He
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), CIMMYT-China Office, 12 Zhongguancun South Street, Beijing, 100081, China
| | - Huihui Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), CIMMYT-China Office, 12 Zhongguancun South Street, Beijing, 100081, China.
- Nanfan Research Institute, CAAS, Sanya, 572024, Hainan, China.
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Yang CJ, Ladejobi O, Mott R, Powell W, Mackay I. Analysis of historical selection in winter wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:3005-3023. [PMID: 35864201 PMCID: PMC9482581 DOI: 10.1007/s00122-022-04163-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
KEY MESSAGE Modeling of the distribution of allele frequency over year of variety release identifies major loci involved in historical breeding of winter wheat. Winter wheat is a major crop with a rich selection history in the modern era of crop breeding. Genetic gains across economically important traits like yield have been well characterized and are the major force driving its production. Winter wheat is also an excellent model for analyzing historical genetic selection. As a proof of concept, we analyze two major collections of winter wheat varieties that were bred in Western Europe from 1916 to 2010, namely the Triticeae Genome (TG) and WAGTAIL panels, which include 333 and 403 varieties, respectively. We develop and apply a selection mapping approach, Regression of Alleles on Years (RALLY), in these panels, as well as in simulated populations. RALLY maps loci under sustained historical selection by using a simple logistic model to regress allele counts on years of variety release. To control for drift-induced allele frequency change, we develop a hybrid approach of genomic control and delta control. Within the TG panel, we identify 22 significant RALLY quantitative selection loci (QSLs) and estimate the local heritabilities for 12 traits across these QSLs. By correlating predicted marker effects with RALLY regression estimates, we show that alleles whose frequencies have increased over time are heavily biased toward conferring positive yield effect, but negative effects in flowering time, lodging, plant height and grain protein content. Altogether, our results (1) demonstrate the use of RALLY to identify selected genomic regions while controlling for drift, and (2) reveal key patterns in the historical selection in winter wheat and guide its future breeding.
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Affiliation(s)
- Chin Jian Yang
- Scotland's Rural College (SRUC), Kings Buildings, West Mains Road, Edinburgh, EH9 3JG, UK
| | - Olufunmilayo Ladejobi
- Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, UK
| | - Richard Mott
- Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT, UK
| | - Wayne Powell
- Scotland's Rural College (SRUC), Kings Buildings, West Mains Road, Edinburgh, EH9 3JG, UK
| | - Ian Mackay
- Scotland's Rural College (SRUC), Kings Buildings, West Mains Road, Edinburgh, EH9 3JG, UK.
- IMplant Consultancy Ltd, Chelmsford, UK.
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Bhat JA, Adeboye KA, Ganie SA, Barmukh R, Hu D, Varshney RK, Yu D. Genome-wide association study, haplotype analysis, and genomic prediction reveal the genetic basis of yield-related traits in soybean (Glycine max L.). Front Genet 2022; 13:953833. [PMID: 36419833 PMCID: PMC9677453 DOI: 10.3389/fgene.2022.953833] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/22/2022] [Indexed: 11/09/2022] Open
Abstract
Identifying the genetic components underlying yield-related traits in soybean is crucial for improving its production and productivity. Here, 211 soybean genotypes were evaluated across six environments for four yield-related traits, including seed yield per plant (SYP), number of pods per plant number of seeds per plant and 100-seed weight (HSW). Genome-wide association study (GWAS) and genomic prediction (GP) analyses were performed using 12,617 single nucleotide polymorphism markers from NJAU 355K SoySNP Array. A total of 57 SNPs were significantly associated with four traits across six environments and a combined environment using five Genome-wide association study models. Out of these, six significant SNPs were consistently identified in more than three environments using multiple GWAS models. The genomic regions (±670 kb) flanking these six consistent SNPs were considered stable QTL regions. Gene annotation and in silico expression analysis revealed 15 putative genes underlying the stable QTLs that might regulate soybean yield. Haplotype analysis using six significant SNPs revealed various allelic combinations regulating diverse phenotypes for the studied traits. Furthermore, the GP analysis revealed that accurate breeding values for the studied soybean traits is attainable at an earlier generation. Our study paved the way for increasing soybean yield performance within a short breeding cycle.
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Affiliation(s)
- Javaid Akhter Bhat
- Soybean Research Institution, National Center for Soybean Improvement, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
- International Genome Center, Jiangsu University, Zhenjiang, China
- *Correspondence: Javaid Akhter Bhat, ; Rajeev K. Varshney, ; Deyue Yu,
| | | | - Showkat Ahmad Ganie
- Plant Molecular Science and Centre of Systems and Synthetic Biology, Department of Biological Sciences, Royal Holloway University of London, Surrey, United Kingdom
| | - Rutwik Barmukh
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Dezhou Hu
- Soybean Research Institution, National Center for Soybean Improvement, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
- Murdoch’s Centre for Crop & Food Innovation, State Agricultural Biotechnology Centre, Food Futures Institute, Murdoch University, Perth, WA, Australia
- *Correspondence: Javaid Akhter Bhat, ; Rajeev K. Varshney, ; Deyue Yu,
| | - Deyue Yu
- Soybean Research Institution, National Center for Soybean Improvement, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
- *Correspondence: Javaid Akhter Bhat, ; Rajeev K. Varshney, ; Deyue Yu,
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Genomic Prediction Accuracy of Stripe Rust in Six Spring Wheat Populations by Modeling Genotype by Environment Interaction. PLANTS 2022; 11:plants11131736. [PMID: 35807690 PMCID: PMC9269065 DOI: 10.3390/plants11131736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 06/27/2022] [Accepted: 06/28/2022] [Indexed: 11/17/2022]
Abstract
Some previous studies have assessed the predictive ability of genome-wide selection on stripe (yellow) rust resistance in wheat, but the effect of genotype by environment interaction (GEI) in prediction accuracies has not been well studied in diverse genetic backgrounds. Here, we compared the predictive ability of a model based on phenotypic data only (M1), the main effect of phenotype and molecular markers (M2), and a model that incorporated GEI (M3) using three cross-validations (CV1, CV2, and CV0) scenarios of interest to breeders in six spring wheat populations. Each population was evaluated at three to eight field nurseries and genotyped with either the DArTseq technology or the wheat 90K single nucleotide polymorphism arrays, of which a subset of 1,058- 23,795 polymorphic markers were used for the analyses. In the CV1 scenario, the mean prediction accuracies of the M1, M2, and M3 models across the six populations varied from −0.11 to −0.07, from 0.22 to 0.49, and from 0.19 to 0.48, respectively. Mean accuracies obtained using the M3 model in the CV1 scenario were significantly greater than the M2 model in two populations, the same in three populations, and smaller in one population. In both the CV2 and CV0 scenarios, the mean prediction accuracies of the three models varied from 0.53 to 0.84 and were not significantly different in all populations, except the Attila/CDC Go in the CV2, where the M3 model gave greater accuracy than both the M1 and M2 models. Overall, the M3 model increased prediction accuracies in some populations by up to 12.4% and decreased accuracy in others by up to 17.4%, demonstrating inconsistent results among genetic backgrounds that require considering each population separately. This is the first comprehensive genome-wide prediction study that investigated details of the effect of GEI on stripe rust resistance across diverse spring wheat populations.
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Juliana P, He X, Poland J, Roy KK, Malaker PK, Mishra VK, Chand R, Shrestha S, Kumar U, Roy C, Gahtyari NC, Joshi AK, Singh RP, Singh PK. Genomic selection for spot blotch in bread wheat breeding panels, full-sibs and half-sibs and index-based selection for spot blotch, heading and plant height. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1965-1983. [PMID: 35416483 PMCID: PMC9205839 DOI: 10.1007/s00122-022-04087-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 03/17/2022] [Indexed: 06/14/2023]
Abstract
KEY MESSAGE Genomic selection is a promising tool to select for spot blotch resistance and index-based selection can simultaneously select for spot blotch resistance, heading and plant height. A major biotic stress challenging bread wheat production in regions characterized by humid and warm weather is spot blotch caused by the fungus Bipolaris sorokiniana. Since genomic selection (GS) is a promising selection tool, we evaluated its potential for spot blotch in seven breeding panels comprising 6736 advanced lines from the International Maize and Wheat Improvement Center. Our results indicated moderately high mean genomic prediction accuracies of 0.53 and 0.40 within and across breeding panels, respectively which were on average 177.6% and 60.4% higher than the mean accuracies from fixed effects models using selected spot blotch loci. Genomic prediction was also evaluated in full-sibs and half-sibs panels and sibs were predicted with the highest mean accuracy (0.63) from a composite training population with random full-sibs and half-sibs. The mean accuracies when full-sibs were predicted from other full-sibs within families and when full-sibs panels were predicted from other half-sibs panels were 0.47 and 0.44, respectively. Comparison of GS with phenotypic selection (PS) of the top 10% of resistant lines suggested that GS could be an ideal tool to discard susceptible lines, as greater than 90% of the susceptible lines discarded by PS were also discarded by GS. We have also reported the evaluation of selection indices to simultaneously select non-late and non-tall genotypes with low spot blotch phenotypic values and genomic-estimated breeding values. Overall, this study demonstrates the potential of integrating GS and index-based selection for improving spot blotch resistance in bread wheat.
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Affiliation(s)
- Philomin Juliana
- Borlaug Institute for South Asia (BISA), Ludhiana, Punjab, India
| | - Xinyao He
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico, DF, Mexico
| | - Jesse Poland
- Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Krishna K Roy
- Bangladesh Wheat and Maize Research Institute, Nashipur, Dinajpur, 5200, Bangladesh
| | - Paritosh K Malaker
- Bangladesh Wheat and Maize Research Institute, Nashipur, Dinajpur, 5200, Bangladesh
| | - Vinod K Mishra
- Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
| | - Ramesh Chand
- Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
| | - Sandesh Shrestha
- Department of Plant Pathology, Wheat Genetics Resource Center, Kansas State University, Manhattan, KS, USA
| | - Uttam Kumar
- Borlaug Institute for South Asia (BISA), Ludhiana, Punjab, India
| | - Chandan Roy
- Department of Plant Breeding and Genetics, Bihar Agricultural University, Sabour, Bihar, 813210, India
| | - Navin C Gahtyari
- ICAR-Vivekanand Parvatiya Krishi Anushandhan Sansthan, Almora, Uttarakhand, 263601, India
| | - Arun K Joshi
- Borlaug Institute for South Asia (BISA), Ludhiana, Punjab, India
- CIMMYT-India, NASC Complex, DPS Marg, New Delhi, India
| | - Ravi P Singh
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico, DF, Mexico.
| | - Pawan K Singh
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico, DF, Mexico.
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Bentley AR, Chen C, D’Agostino N. Editorial: Genome Wide Association Studies and Genomic Selection for Crop Improvement in the Era of Big Data. Front Genet 2022; 13:873060. [PMID: 35669194 PMCID: PMC9164124 DOI: 10.3389/fgene.2022.873060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 05/04/2022] [Indexed: 12/02/2022] Open
Affiliation(s)
- Alison R. Bentley
- International Wheat and Maize Improvement Center (CIMMYT), El Batan, Mexico
- NIAB, Cambridge, United Kingdom
- *Correspondence: Alison R. Bentley, ; Charles Chen, ; Nunzio D’Agostino,
| | - Charles Chen
- Department of Biochemistry and Molecular Biology, 246 Noble Research Center, Oklahoma State University, Stillwater, OK, United States
- *Correspondence: Alison R. Bentley, ; Charles Chen, ; Nunzio D’Agostino,
| | - Nunzio D’Agostino
- Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy
- *Correspondence: Alison R. Bentley, ; Charles Chen, ; Nunzio D’Agostino,
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Danilevicz MF, Gill M, Anderson R, Batley J, Bennamoun M, Bayer PE, Edwards D. Plant Genotype to Phenotype Prediction Using Machine Learning. Front Genet 2022; 13:822173. [PMID: 35664329 PMCID: PMC9159391 DOI: 10.3389/fgene.2022.822173] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 03/07/2022] [Indexed: 12/13/2022] Open
Abstract
Genomic prediction tools support crop breeding based on statistical methods, such as the genomic best linear unbiased prediction (GBLUP). However, these tools are not designed to capture non-linear relationships within multi-dimensional datasets, or deal with high dimension datasets such as imagery collected by unmanned aerial vehicles. Machine learning (ML) algorithms have the potential to surpass the prediction accuracy of current tools used for genotype to phenotype prediction, due to their capacity to autonomously extract data features and represent their relationships at multiple levels of abstraction. This review addresses the challenges of applying statistical and machine learning methods for predicting phenotypic traits based on genetic markers, environment data, and imagery for crop breeding. We present the advantages and disadvantages of explainable model structures, discuss the potential of machine learning models for genotype to phenotype prediction in crop breeding, and the challenges, including the scarcity of high-quality datasets, inconsistent metadata annotation and the requirements of ML models.
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Affiliation(s)
- Monica F. Danilevicz
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Mitchell Gill
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Robyn Anderson
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Mohammed Bennamoun
- School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, Australia
| | - Philipp E. Bayer
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
- *Correspondence: David Edwards,
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Lozada DN, Bosland PW, Barchenger DW, Haghshenas-Jaryani M, Sanogo S, Walker S. Chile Pepper ( Capsicum) Breeding and Improvement in the "Multi-Omics" Era. FRONTIERS IN PLANT SCIENCE 2022; 13:879182. [PMID: 35592583 PMCID: PMC9113053 DOI: 10.3389/fpls.2022.879182] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 04/12/2022] [Indexed: 06/15/2023]
Abstract
Chile pepper (Capsicum spp.) is a major culinary, medicinal, and economic crop in most areas of the world. For more than hundreds of years, chile peppers have "defined" the state of New Mexico, USA. The official state question, "Red or Green?" refers to the preference for either red or the green stage of chile pepper, respectively, reflects the value of these important commodities. The presence of major diseases, low yields, decreased acreages, and costs associated with manual labor limit production in all growing regions of the world. The New Mexico State University (NMSU) Chile Pepper Breeding Program continues to serve as a key player in the development of improved chile pepper varieties for growers and in discoveries that assist plant breeders worldwide. Among the traits of interest for genetic improvement include yield, disease resistance, flavor, and mechanical harvestability. While progress has been made, the use of conventional breeding approaches has yet to fully address producer and consumer demand for these traits in available cultivars. Recent developments in "multi-omics," that is, the simultaneous application of multiple omics approaches to study biological systems, have allowed the genetic dissection of important phenotypes. Given the current needs and production constraints, and the availability of multi-omics tools, it would be relevant to examine the application of these approaches in chile pepper breeding and improvement. In this review, we summarize the major developments in chile pepper breeding and present novel tools that can be implemented to facilitate genetic improvement. In the future, chile pepper improvement is anticipated to be more data and multi-omics driven as more advanced genetics, breeding, and phenotyping tools are developed.
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Affiliation(s)
- Dennis N. Lozada
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, United States
- Chile Pepper Institute, New Mexico State University, Las Cruces, NM, United States
| | - Paul W. Bosland
- Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, United States
- Chile Pepper Institute, New Mexico State University, Las Cruces, NM, United States
| | | | - Mahdi Haghshenas-Jaryani
- Department of Mechanical and Aerospace Engineering, New Mexico State University, Las Cruces, NM, United States
| | - Soumaila Sanogo
- Department of Entomology, Plant Pathology and Weed Science, New Mexico State University, Las Cruces, NM, United States
| | - Stephanie Walker
- Chile Pepper Institute, New Mexico State University, Las Cruces, NM, United States
- Department of Extension Plant Sciences, New Mexico State University, Las Cruces, NM, United States
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Mathew B, Hauptmann A, Léon J, Sillanpää MJ. NeuralLasso: Neural Networks Meet Lasso in Genomic Prediction. FRONTIERS IN PLANT SCIENCE 2022; 13:800161. [PMID: 35574107 PMCID: PMC9100816 DOI: 10.3389/fpls.2022.800161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 03/18/2022] [Indexed: 06/15/2023]
Abstract
Prediction of complex traits based on genome-wide marker information is of central importance for both animal and plant breeding. Numerous models have been proposed for the prediction of complex traits and still considerable effort has been given to improve the prediction accuracy of these models, because various genetics factors like additive, dominance and epistasis effects can influence of the prediction accuracy of such models. Recently machine learning (ML) methods have been widely applied for prediction in both animal and plant breeding programs. In this study, we propose a new algorithm for genomic prediction which is based on neural networks, but incorporates classical elements of LASSO. Our new method is able to account for the local epistasis (higher order interaction between the neighboring markers) in the prediction. We compare the prediction accuracy of our new method with the most commonly used prediction methods, such as BayesA, BayesB, Bayesian Lasso (BL), genomic BLUP and Elastic Net (EN) using the heterogenous stock mouse and rice field data sets.
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Affiliation(s)
- Boby Mathew
- Bayer CropScience, Monheim am Rhein, Germany
- Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
| | - Andreas Hauptmann
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
- Department of Computer Science, University College London, London, United Kingdom
| | - Jens Léon
- Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany
| | - Mikko J. Sillanpää
- Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
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44
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Genomic Prediction of Complex Traits in Perennial Plants: A Case for Forest Trees. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2467:493-520. [PMID: 35451788 DOI: 10.1007/978-1-0716-2205-6_18] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
This chapter provides an overview of the genomic selection progress in long-lived forest tree species. Factors affecting the prediction accuracy in genomic prediction are assessed with examples from empirical studies. Infrastructure and resources required for the implementation of genomic selection are evaluated. Some general guidelines are provided for the successful application of genomic selection in forest tree breeding programs.
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45
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Gill M, Anderson R, Hu H, Bennamoun M, Petereit J, Valliyodan B, Nguyen HT, Batley J, Bayer PE, Edwards D. Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction. BMC PLANT BIOLOGY 2022; 22:180. [PMID: 35395721 PMCID: PMC8991976 DOI: 10.1186/s12870-022-03559-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/21/2022] [Indexed: 05/26/2023]
Abstract
Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few approaches for genotype to phenotype prediction compare machine learning with deep learning and further interpret the models that support the predictions. This study uses genome wide molecular markers and traits across 1110 soybean individuals to develop accurate prediction models. For 13/14 sets of predictions, XGBoost or random forest outperformed deep learning models in prediction performance. Top ranked SNPs by F-score were identified from XGBoost, and with further investigation found overlap with significantly associated loci identified from GWAS and previous literature. Feature importance rankings were used to reduce marker input by up to 90%, and subsequent models maintained or improved their prediction performance. These findings support interpretable machine learning as an approach for genomic based prediction of traits in soybean and other crops.
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Affiliation(s)
- Mitchell Gill
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Robyn Anderson
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Haifei Hu
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Mohammed Bennamoun
- Department of Computer Science and Software Engineering, The University of Western Australia, Perth, WA, Australia
| | - Jakob Petereit
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Babu Valliyodan
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri, Columbia, MO, 65211, USA
- Department of Agriculture and Environmental Sciences, Lincoln University, Jefferson City, MO, 65101, USA
| | - Henry T Nguyen
- Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri, Columbia, MO, 65211, USA
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Philipp E Bayer
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia.
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Kastally C, Niskanen AK, Perry A, Kujala ST, Avia K, Cervantes S, Haapanen M, Kesälahti R, Kumpula TA, Mattila TM, Ojeda DI, Tyrmi JS, Wachowiak W, Cavers S, Kärkkäinen K, Savolainen O, Pyhäjärvi T. Taming the massive genome of Scots pine with PiSy50k, a new genotyping array for conifer research. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 109:1337-1350. [PMID: 34897859 PMCID: PMC9303803 DOI: 10.1111/tpj.15628] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 11/05/2021] [Accepted: 12/02/2021] [Indexed: 06/14/2023]
Abstract
Pinus sylvestris (Scots pine) is the most widespread coniferous tree in the boreal forests of Eurasia, with major economic and ecological importance. However, its large and repetitive genome presents a challenge for conducting genome-wide analyses such as association studies, genetic mapping and genomic selection. We present a new 50K single-nucleotide polymorphism (SNP) genotyping array for Scots pine research, breeding and other applications. To select the SNP set, we first genotyped 480 Scots pine samples on a 407 540 SNP screening array and identified 47 712 high-quality SNPs for the final array (called 'PiSy50k'). Here, we provide details of the design and testing, as well as allele frequency estimates from the discovery panel, functional annotation, tissue-specific expression patterns and expression level information for the SNPs or corresponding genes, when available. We validated the performance of the PiSy50k array using samples from Finland and Scotland. Overall, 39 678 (83.2%) SNPs showed low error rates (mean = 0.9%). Relatedness estimates based on array genotypes were consistent with the expected pedigrees, and the level of Mendelian error was negligible. In addition, array genotypes successfully discriminate between Scots pine populations of Finnish and Scottish origins. The PiSy50k SNP array will be a valuable tool for a wide variety of future genetic studies and forestry applications.
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Affiliation(s)
- Chedly Kastally
- Department of Ecology and GeneticsUniversity of OuluP.O. Box 300090014OuluFinland
| | - Alina K. Niskanen
- Department of Ecology and GeneticsUniversity of OuluP.O. Box 300090014OuluFinland
| | - Annika Perry
- UK Centre for Ecology & HydrologyBush EstatePenicuikMidlothianEH26 0QBUK
| | - Sonja T. Kujala
- Natural Resources Institute Finland (Luke)Paavo Havaksen tie 390570OuluFinland
| | - Komlan Avia
- Université de StrasbourgINRAESVQV UMR‐A 1131F‐68000ColmarFrance
| | - Sandra Cervantes
- Department of Ecology and GeneticsUniversity of OuluP.O. Box 300090014OuluFinland
| | - Matti Haapanen
- Natural Resources Institute Finland (Luke)Latokartanonkaari 9FI‐00790HelsinkiFinland
| | - Robert Kesälahti
- Department of Ecology and GeneticsUniversity of OuluP.O. Box 300090014OuluFinland
| | - Timo A. Kumpula
- Department of Ecology and GeneticsUniversity of OuluP.O. Box 300090014OuluFinland
| | - Tiina M. Mattila
- Department of Ecology and GeneticsUniversity of OuluP.O. Box 300090014OuluFinland
- Department of Organismal BiologyEBCUppsala UniversityNorbyvägen 18 AUppsala752 36Sweden
| | - Dario I. Ojeda
- Department of Ecology and GeneticsUniversity of OuluP.O. Box 300090014OuluFinland
- Norwegian Institute of Bioeconomy ResearchP.O. Box 115Ås1431Norway
| | - Jaakko S. Tyrmi
- Department of Ecology and GeneticsUniversity of OuluP.O. Box 300090014OuluFinland
| | - Witold Wachowiak
- Institute of Environmental BiologyFaculty of BiologyAdam Mickiewicz University in PoznańUniwersytetu Poznańskiego 661‐614PoznańPoland
| | - Stephen Cavers
- UK Centre for Ecology & HydrologyBush EstatePenicuikMidlothianEH26 0QBUK
| | - Katri Kärkkäinen
- Natural Resources Institute Finland (Luke)Paavo Havaksen tie 390570OuluFinland
| | - Outi Savolainen
- Department of Ecology and GeneticsUniversity of OuluP.O. Box 300090014OuluFinland
| | - Tanja Pyhäjärvi
- Department of Ecology and GeneticsUniversity of OuluP.O. Box 300090014OuluFinland
- Department of Forest SciencesUniversity of HelsinkiP.O. Box 2700014HelsinkiFinland
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Roth M, Beugnot A, Mary-Huard T, Moreau L, Charcosset A, Fiévet JB. Improving genomic predictions with inbreeding and nonadditive effects in two admixed maize hybrid populations in single and multienvironment contexts. Genetics 2022; 220:6527635. [PMID: 35150258 PMCID: PMC8982028 DOI: 10.1093/genetics/iyac018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 01/28/2022] [Indexed: 11/12/2022] Open
Abstract
Genetic admixture, resulting from the recombination between structural groups, is frequently encountered in breeding populations. In hybrid breeding, crossing admixed lines can generate substantial nonadditive genetic variance and contrasted levels of inbreeding which can impact trait variation. This study aimed at testing recent methodological developments for the modeling of inbreeding and nonadditive effects in order to increase prediction accuracy in admixed populations. Using two maize (Zea mays L.) populations of hybrids admixed between dent and flint heterotic groups, we compared a suite of five genomic prediction models incorporating (or not) parameters accounting for inbreeding and nonadditive effects with the natural and orthogonal interaction approach in single and multienvironment contexts. In both populations, variance decompositions showed the strong impact of inbreeding on plant yield, height, and flowering time which was supported by the superiority of prediction models incorporating this effect (+0.038 in predictive ability for mean yield). In most cases dominance variance was reduced when inbreeding was accounted for. The model including additivity, dominance, epistasis, and inbreeding effects appeared to be the most robust for prediction across traits and populations (+0.054 in predictive ability for mean yield). In a multienvironment context, we found that the inclusion of nonadditive and inbreeding effects was advantageous when predicting hybrids not yet observed in any environment. Overall, comparing variance decompositions was helpful to guide model selection for genomic prediction. Finally, we recommend the use of models including inbreeding and nonadditive parameters following the natural and orthogonal interaction approach to increase prediction accuracy in admixed populations.
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Affiliation(s)
- Morgane Roth
- Plant Breeding Research Division, Agroscope, Wädenswil, 8820 Zurich, Switzerland,Corresponding author: INRAE GAFL, 67 Allée des Chênes 84140 Montfavet, France.
| | - Aurélien Beugnot
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France,Université Paris-Saclay, INRAE, AgroParisTech, UMR MIA-Paris Paris, 75005 Paris, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Julie B Fiévet
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
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Diepenbrock CH, Tang T, Jines M, Technow F, Lira S, Podlich D, Cooper M, Messina C. Can we harness digital technologies and physiology to hasten genetic gain in US maize breeding? PLANT PHYSIOLOGY 2022; 188:1141-1157. [PMID: 34791474 PMCID: PMC8825268 DOI: 10.1093/plphys/kiab527] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 10/01/2021] [Indexed: 05/26/2023]
Abstract
Plant physiology can offer invaluable insights to accelerate genetic gain. However, translating physiological understanding into breeding decisions has been an ongoing and complex endeavor. Here we demonstrate an approach to leverage physiology and genomics to hasten crop improvement. A half-diallel maize (Zea mays) experiment resulting from crossing 9 elite inbreds was conducted at 17 locations in the USA corn belt and 6 locations at managed stress environments between 2017 and 2019 covering a range of water environments from 377 to 760 mm of evapotranspiration and family mean yields from 542 to 1,874 g m-2. Results from analyses of 35 families and 2,367 hybrids using crop growth models linked to whole-genome prediction (CGM-WGP) demonstrated that CGM-WGP offered a predictive accuracy advantage compared to BayesA for untested genotypes evaluated in untested environments (r = 0.43 versus r = 0.27). In contrast to WGP, CGMs can deal effectively with time-dependent interactions between a physiological process and the environment. To facilitate the selection/identification of traits for modeling yield, an algorithmic approach was introduced. The method was able to identify 4 out of 12 candidate traits known to explain yield variation in maize. The estimation of allelic and physiological values for each genotype using the CGM created in silico phenotypes (e.g. root elongation) and physiological hypotheses that could be tested within the breeding program in an iterative manner. Overall, the approach and results suggest a promising future to fully harness digital technologies, gap analysis, and physiological knowledge to hasten genetic gain by improving predictive skill and definition of breeding goals.
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Affiliation(s)
| | - Tom Tang
- Research & Development, Corteva Agriscience, Johnston, Iowa 50131, USA
| | - Michael Jines
- Research & Development, Corteva Agriscience, Windfall, Indiana 46076, USA
| | - Frank Technow
- Research & Development, Corteva Agriscience, Tavistock, ON N4S 7W1, Canada
| | - Sara Lira
- Research & Development, Corteva Agriscience, Johnston, Iowa 50131, USA
| | - Dean Podlich
- Research & Development, Corteva Agriscience, Johnston, Iowa 50131, USA
| | - Mark Cooper
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Carlos Messina
- Research & Development, Corteva Agriscience, Johnston, Iowa 50131, USA
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Kumar A, Gautam RD, Kumar A, Singh S, Singh S. Understanding the Effect of Different Abiotic Stresses on Wild Marigold ( Tagetes minuta L.) and Role of Breeding Strategies for Developing Tolerant Lines. FRONTIERS IN PLANT SCIENCE 2022; 12:754457. [PMID: 35185943 PMCID: PMC8850357 DOI: 10.3389/fpls.2021.754457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 12/21/2021] [Indexed: 06/14/2023]
Abstract
Wild marigold has a growing demand for its essential oil in the flavor and fragrance industries. It can be grown over a broad range of climates, but the changing climatic conditions lead to abiotic stresses, thus restricting its productivity. Abiotic stresses at elevated levels result in the reduction of germination, growth, and essential oil quality of wild marigold leading to heterogeneous and inferior grades of "Tagetes oil." Drought, salinity, and heavy metal stress at elevated levels have common effects in terms of ROS formation, which are the major cause of growth deterioration in wild marigold. Temperatures above 35°C inhibit seed germination. Irradiance stress reduces the biomass and essential oil yield. Waterlogging adversely affects the survival of wild marigold in high rainfall regions. The application of plant nutrients (fertilizers) modulates the biomass and essential oil yield. Wild marigold employs multiple tolerance mechanisms to cope up with the adverse effects of abiotic stresses such as the increased activity of antioxidants to maintain cellular redox homeostasis, enhanced lipid peroxidation in the cell membrane to maintain cell wall architecture, production of secondary metabolites, and accumulation of osmolytes. In this review, we tried to understand how abiotic stresses affect wild marigold. Understanding the physiological changes and biochemical characteristics of stress tolerance will contribute to the development of stress-tolerant lines of wild marigold.
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Affiliation(s)
- Ajay Kumar
- Academy of Scientific and Innovative Research, CSIR-HRDC, Ghaziabad, India
- Division of Agrotechnology, Council of Scientific and Industrial Research-Institute of Himalayan Bioresource Technology, Kangra, India
| | - Rahul Dev Gautam
- Academy of Scientific and Innovative Research, CSIR-HRDC, Ghaziabad, India
- Division of Agrotechnology, Council of Scientific and Industrial Research-Institute of Himalayan Bioresource Technology, Kangra, India
| | - Ashok Kumar
- Division of Agrotechnology, Council of Scientific and Industrial Research-Institute of Himalayan Bioresource Technology, Kangra, India
| | - Satbeer Singh
- Division of Agrotechnology, Council of Scientific and Industrial Research-Institute of Himalayan Bioresource Technology, Kangra, India
| | - Sanatsujat Singh
- Academy of Scientific and Innovative Research, CSIR-HRDC, Ghaziabad, India
- Division of Agrotechnology, Council of Scientific and Industrial Research-Institute of Himalayan Bioresource Technology, Kangra, India
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50
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Juliana P, He X, Marza F, Islam R, Anwar B, Poland J, Shrestha S, Singh GP, Chawade A, Joshi AK, Singh RP, Singh PK. Genomic Selection for Wheat Blast in a Diversity Panel, Breeding Panel and Full-Sibs Panel. FRONTIERS IN PLANT SCIENCE 2022; 12:745379. [PMID: 35069614 PMCID: PMC8782147 DOI: 10.3389/fpls.2021.745379] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 11/09/2021] [Indexed: 06/14/2023]
Abstract
Wheat blast is an emerging threat to wheat production, due to its recent migration to South Asia and Sub-Saharan Africa. Because genomic selection (GS) has emerged as a promising breeding strategy, the key objective of this study was to evaluate it for wheat blast phenotyped at precision phenotyping platforms in Quirusillas (Bolivia), Okinawa (Bolivia) and Jashore (Bangladesh) using three panels: (i) a diversity panel comprising 172 diverse spring wheat genotypes, (ii) a breeding panel comprising 248 elite breeding lines, and (iii) a full-sibs panel comprising 298 full-sibs. We evaluated two genomic prediction models (the genomic best linear unbiased prediction or GBLUP model and the Bayes B model) and compared the genomic prediction accuracies with accuracies from a fixed effects model (with selected blast-associated markers as fixed effects), a GBLUP + fixed effects model and a pedigree relationships-based model (ABLUP). On average, across all the panels and environments analyzed, the GBLUP + fixed effects model (0.63 ± 0.13) and the fixed effects model (0.62 ± 0.13) gave the highest prediction accuracies, followed by the Bayes B (0.59 ± 0.11), GBLUP (0.55 ± 0.1), and ABLUP (0.48 ± 0.06) models. The high prediction accuracies from the fixed effects model resulted from the markers tagging the 2NS translocation that had a large effect on blast in all the panels. This implies that in environments where the 2NS translocation-based blast resistance is effective, genotyping one to few markers tagging the translocation is sufficient to predict the blast response and genome-wide markers may not be needed. We also observed that marker-assisted selection (MAS) based on a few blast-associated markers outperformed GS as it selected the highest mean percentage (88.5%) of lines also selected by phenotypic selection and discarded the highest mean percentage of lines (91.8%) also discarded by phenotypic selection, across all panels. In conclusion, while this study demonstrates that MAS might be a powerful strategy to select for the 2NS translocation-based blast resistance, we emphasize that further efforts to use genomic tools to identify non-2NS translocation-based blast resistance are critical.
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Affiliation(s)
| | - Xinyao He
- International Maize and Wheat Improvement Center (CIMMYT), Mexico, Mexico
| | - Felix Marza
- Instituto Nacional de Innovación Agropecuaria y Forestal (INIAF), La Paz, Bolivia
| | - Rabiul Islam
- Bangladesh Wheat and Maize Research Institute (BWMRI), Dinajpur, Bangladesh
| | - Babul Anwar
- Bangladesh Wheat and Maize Research Institute (BWMRI), Dinajpur, Bangladesh
| | - Jesse Poland
- Department of Plant Pathology, Wheat Genetics Resource Center, Kansas State University, Manhattan, KS, United States
| | - Sandesh Shrestha
- Department of Plant Pathology, Wheat Genetics Resource Center, Kansas State University, Manhattan, KS, United States
| | - Gyanendra P. Singh
- Indian Council of Agricultural Research (ICAR)-Indian Institute of Wheat and Barley Research, Karnal, India
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Arun K. Joshi
- Borlaug Institute for South Asia (BISA), Ludhiana, India
- CIMMYT-India, New Delhi, India
| | - Ravi P. Singh
- International Maize and Wheat Improvement Center (CIMMYT), Mexico, Mexico
| | - Pawan K. Singh
- International Maize and Wheat Improvement Center (CIMMYT), Mexico, Mexico
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