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Zhang H, Tang Y, Yue Y, Chen Y. Advances in the evolution research and genetic breeding of peanut. Gene 2024; 916:148425. [PMID: 38575102 DOI: 10.1016/j.gene.2024.148425] [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: 11/29/2023] [Revised: 03/15/2024] [Accepted: 04/01/2024] [Indexed: 04/06/2024]
Abstract
Peanut is an important cash crop used in oil, food and feed in our country. The rapid development of sequencing technology has promoted the research on the related aspects of peanut genetic breeding. This paper reviews the research progress of peanut origin and evolution, genetic breeding, molecular markers and their applications, genomics, QTL mapping and genome selection techniques. The main problems of molecular genetic breeding in peanut research worldwide include: the narrow genetic resources of cultivated species, unstable genetic transformation and unclear molecular mechanism of important agronomic traits. Considering the severe challenges regarding the supply of edible oil, and the main problems in peanut production, the urgent research directions of peanut are put forward: The de novo domestication and the exploitation of excellent genes from wild resources to improve modern cultivars; Integration of multi-omics data to enhance the importance of big data in peanut genetics and breeding; Cloning the important genes related to peanut agronomic traits and analyzing their fine regulation mechanisms; Precision molecular design breeding and using gene editing technology to accurately improve the key traits of peanut.
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Affiliation(s)
- Hui Zhang
- College of Agriculture, South China Agricultural University, Guangzhou 510642, China.
| | - Yueyi Tang
- Shandong Peanut Research Institute, Qingdao 266100, China
| | - Yunlai Yue
- College of Agriculture, South China Agricultural University, Guangzhou 510642, China
| | - Yong Chen
- College of Agriculture, South China Agricultural University, Guangzhou 510642, China.
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2
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Jamil S, Ahmad S, Shahzad R, Umer N, Kanwal S, Rehman HM, Rana IA, Atif RM. Leveraging Multiomics Insights and Exploiting Wild Relatives' Potential for Drought and Heat Tolerance in Maize. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024. [PMID: 38980762 DOI: 10.1021/acs.jafc.4c01375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
Climate change, particularly drought and heat stress, may slash agricultural productivity by 25.7% by 2080, with maize being the hardest hit. Therefore, unraveling the molecular nature of plant responses to these stressors is vital for the development of climate-smart maize. This manuscript's primary objective was to examine how maize plants respond to these stresses, both individually and in combination. Additionally, the paper delved into harnessing the potential of maize wild relatives as a valuable genetic resource and leveraging AI-based technologies to boost maize resilience. The role of multiomics approaches particularly genomics and transcriptomics in dissecting the genetic basis of stress tolerance was also highlighted. The way forward was proposed to utilize a bunch of information obtained through omics technologies by an interdisciplinary state-of-the-art forward-looking big-data, cyberagriculture system, and AI-based approach to orchestrate the development of climate resilient maize genotypes.
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Affiliation(s)
- Shakra Jamil
- Agricultural Biotechnology Research Institute, Ayub Agricultural Research Institute, Faisalabad 38000, Pakistan
| | - Shakeel Ahmad
- Seed Centre and Plant Genetic Resources Bank Ministry of Environment, Water and Agriculture, Riyadh 14712, Saudi Arabia
| | - Rahil Shahzad
- Agricultural Biotechnology Research Institute, Ayub Agricultural Research Institute, Faisalabad 38000, Pakistan
| | - Noroza Umer
- Dr. Ikram ul Haq - Institute of Industrial Biotechnology, Government College University, Lahore 54590, Pakistan
| | - Shamsa Kanwal
- Agricultural Biotechnology Research Institute, Ayub Agricultural Research Institute, Faisalabad 38000, Pakistan
| | - Hafiz Mamoon Rehman
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad 38000, Pakistan
| | - Iqrar Ahmad Rana
- Centre for Advanced Studies in Agriculture and Food Security, University of Agriculture, Faisalabad 38000, Pakistan
| | - Rana Muhammad Atif
- Department of Plant Sciences, University of California Davis, California 95616, United States
- Department of Plant Breeding and Genetics, University of Agriculture, Faisalabad 38000, Pakistan
- Precision Agriculture and Analytics Lab, Centre for Advanced Studies in Agriculture and Food Security, National Centre in Big Data and Cloud Computing, University of Agriculture, Faisalabad 38000, Pakistan
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3
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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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Alemu A, Åstrand J, Montesinos-López OA, Isidro Y Sánchez J, Fernández-Gónzalez J, Tadesse W, Vetukuri RR, Carlsson AS, Ceplitis A, Crossa J, Ortiz R, Chawade A. Genomic selection in plant breeding: Key factors shaping two decades of progress. MOLECULAR PLANT 2024; 17:552-578. [PMID: 38475993 DOI: 10.1016/j.molp.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/22/2024] [Accepted: 03/08/2024] [Indexed: 03/14/2024]
Abstract
Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.
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Affiliation(s)
- Admas Alemu
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Johanna Åstrand
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden; Lantmännen Lantbruk, Svalöv, Sweden
| | | | - Julio Isidro Y Sánchez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Javier Fernández-Gónzalez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Wuletaw Tadesse
- International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
| | - Ramesh R Vetukuri
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Anders S Carlsson
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México-Veracruz, Texcoco, México 52640, Mexico
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
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5
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Fernández-González J, Haquin B, Combes E, Bernard K, Allard A, Isidro Y Sánchez J. Maximizing efficiency in sunflower breeding through historical data optimization. PLANT METHODS 2024; 20:42. [PMID: 38493115 PMCID: PMC10943787 DOI: 10.1186/s13007-024-01151-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 01/30/2024] [Indexed: 03/18/2024]
Abstract
Genomic selection (GS) has become an increasingly popular tool in plant breeding programs, propelled by declining genotyping costs, an increase in computational power, and rediscovery of the best linear unbiased prediction methodology over the past two decades. This development has led to an accumulation of extensive historical datasets with genotypic and phenotypic information, triggering the question of how to best utilize these datasets. Here, we investigate whether all available data or a subset should be used to calibrate GS models for across-year predictions in a 7-year dataset of a commercial hybrid sunflower breeding program. We employed a multi-objective optimization approach to determine the ideal years to include in the training set (TRS). Next, for a given combination of TRS years, we further optimized the TRS size and its genetic composition. We developed the Min_GRM size optimization method which consistently found the optimal TRS size, reducing dimensionality by 20% with an approximately 1% loss in predictive ability. Additionally, the Tails_GEGVs algorithm displayed potential, outperforming the use of all data by using just 60% of it for grain yield, a high-complexity, low-heritability trait. Moreover, maximizing the genetic diversity of the TRS resulted in a consistent predictive ability across the entire range of genotypic values in the test set. Interestingly, the Tails_GEGVs algorithm, due to its ability to leverage heterogeneity, enhanced predictive performance for key hybrids with extreme genotypic values. Our study provides new insights into the optimal utilization of historical data in plant breeding programs, resulting in improved GS model predictive ability.
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Affiliation(s)
- Javier Fernández-González
- Centro de Biotecnologia y Genómica de Plantas (CBGP, UPM-INIA)-Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Universidad Politécnica de Madrid (UPM), Campus de Montegancedo-UPM, Pozuelo de Alarcón, Madrid, 28223, Spain.
| | | | | | | | | | - Julio Isidro Y Sánchez
- Centro de Biotecnologia y Genómica de Plantas (CBGP, UPM-INIA)-Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Universidad Politécnica de Madrid (UPM), Campus de Montegancedo-UPM, Pozuelo de Alarcón, Madrid, 28223, Spain.
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Ndlovu N, Kachapur RM, Beyene Y, Das B, Ogugo V, Makumbi D, Spillane C, McKeown PC, Prasanna BM, Gowda M. Linkage mapping and genomic prediction of grain quality traits in tropical maize ( Zea mays L.). Front Genet 2024; 15:1353289. [PMID: 38456017 PMCID: PMC10918846 DOI: 10.3389/fgene.2024.1353289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 02/07/2024] [Indexed: 03/09/2024] Open
Abstract
The suboptimal productivity of maize systems in sub-Saharan Africa (SSA) is a pressing issue, with far-reaching implications for food security, nutrition, and livelihood sustainability within the affected smallholder farming communities. Dissecting the genetic basis of grain protein, starch and oil content can increase our understanding of the governing genetic systems, improve the efficacy of future breeding schemes and optimize the end-use quality of tropical maize. Here, four bi-parental maize populations were evaluated in field trials in Kenya and genotyped with mid-density single nucleotide polymorphism (SNP) markers. Genotypic (G), environmental (E) and G×E variations were found to be significant for all grain quality traits. Broad sense heritabilities exhibited substantial variation (0.18-0.68). Linkage mapping identified multiple quantitative trait loci (QTLs) for the studied grain quality traits: 13, 7, 33, 8 and 2 QTLs for oil content, protein content, starch content, grain texture and kernel weight, respectively. The co-localization of QTLs identified in our research suggests the presence of shared genetic factors or pleiotropic effects, implying that specific genomic regions influence the expression of multiple grain quality traits simultaneously. Genomic prediction accuracies were moderate to high for the studied traits. Our findings highlight the polygenic nature of grain quality traits and demonstrate the potential of genomic selection to enhance genetic gains in maize breeding. Furthermore, the identified genomic regions and single nucleotide polymorphism markers can serve as the groundwork for investigating candidate genes that regulate grain quality traits in tropical maize. This, in turn, can facilitate the implementation of marker-assisted selection (MAS) in breeding programs focused on improving grain nutrient levels.
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Affiliation(s)
- Noel Ndlovu
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, Galway, Ireland
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Rajashekar M. Kachapur
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
- University of Agricultural Sciences, Dharwad, Karnataka, India
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Biswanath Das
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Veronica Ogugo
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Dan Makumbi
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Charles Spillane
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, Galway, Ireland
| | - Peter C. McKeown
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, Galway, Ireland
| | | | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
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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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Fernández-González J, Akdemir D, Isidro Y Sánchez J. A comparison of methods for training population optimization in genomic selection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:30. [PMID: 36892603 PMCID: PMC9998580 DOI: 10.1007/s00122-023-04265-6] [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: 06/30/2022] [Accepted: 11/21/2022] [Indexed: 06/18/2023]
Abstract
Maximizing CDmean and Avg_GRM_self were the best criteria for training set optimization. A training set size of 50-55% (targeted) or 65-85% (untargeted) is needed to obtain 95% of the accuracy. With the advent of genomic selection (GS) as a widespread breeding tool, mechanisms to efficiently design an optimal training set for GS models became more relevant, since they allow maximizing the accuracy while minimizing the phenotyping costs. The literature described many training set optimization methods, but there is a lack of a comprehensive comparison among them. This work aimed to provide an extensive benchmark among optimization methods and optimal training set size by testing a wide range of them in seven datasets, six different species, different genetic architectures, population structure, heritabilities, and with several GS models to provide some guidelines about their application in breeding programs. Our results showed that targeted optimization (uses information from the test set) performed better than untargeted (does not use test set data), especially when heritability was low. The mean coefficient of determination was the best targeted method, although it was computationally intensive. Minimizing the average relationship within the training set was the best strategy for untargeted optimization. Regarding the optimal training set size, maximum accuracy was obtained when the training set was the entire candidate set. Nevertheless, a 50-55% of the candidate set was enough to reach 95-100% of the maximum accuracy in the targeted scenario, while we needed a 65-85% for untargeted optimization. Our results also suggested that a diverse training set makes GS robust against population structure, while including clustering information was less effective. The choice of the GS model did not have a significant influence on the prediction accuracies.
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Affiliation(s)
- Javier Fernández-González
- Centro de Biotecnologia y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223, Madrid, Spain.
| | - Deniz Akdemir
- CIBMTR (Center for International Blood and Marrow Transplant Research), National Marrow Donor Program/Be The Match, Minneapolis, USA
| | - Julio Isidro Y Sánchez
- Centro de Biotecnologia y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223, Madrid, Spain.
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Li T, Jiang S, Fu R, Wang X, Cheng Q, Jiang S. IP4GS: Bringing genomic selection analysis to breeders. FRONTIERS IN PLANT SCIENCE 2023; 14:1131493. [PMID: 36950355 PMCID: PMC10025548 DOI: 10.3389/fpls.2023.1131493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/25/2022] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Genomic selection (GS), a strategy to use genotypes to predict phenotypes via statistical or machine learning models, has become a routine practice in plant breeding programs. GS can speed up the genetic gain by reducing phenotyping costs and/or shortening the breeding cycles. GS analysis is complicated involving data clean up and formatting, training and test population analysis, model selection and evaluation, and parameter optimization. In addition, GS analysis also requires some programming skills and knowledge of statistical modeling. Thus, we need a more practical GS tools for breeders. To alleviate this difficulty, we developed the web-based platform IP4GS (https://ngdc.cncb.ac.cn/ip4gs/), which offers a user-friendly interface to perform GS analysis simply through point-and-click actions. IP4GS currently includes seven commonly used models, eleven evaluation metrics, and visualization modules, offering great convenience for plant breeders with limited bioinformatics knowledge to apply GS analysis.
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Affiliation(s)
| | | | | | | | - Qian Cheng
- *Correspondence: Qian Cheng, ; Shuqin Jiang,
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10
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Genetic trends in CIMMYT's tropical maize breeding pipelines. Sci Rep 2022; 12:20110. [PMID: 36418412 PMCID: PMC9684471 DOI: 10.1038/s41598-022-24536-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 11/16/2022] [Indexed: 11/24/2022] Open
Abstract
Fostering a culture of continuous improvement through regular monitoring of genetic trends in breeding pipelines is essential to improve efficiency and increase accountability. This is the first global study to estimate genetic trends across the International Maize and Wheat Improvement Center (CIMMYT) tropical maize breeding pipelines in eastern and southern Africa (ESA), South Asia, and Latin America over the past decade. Data from a total of 4152 advanced breeding trials and 34,813 entries, conducted at 1331 locations in 28 countries globally, were used for this study. Genetic trends for grain yield reached up to 138 kg ha-1 yr-1 in ESA, 118 kg ha-1 yr-1 South Asia and 143 kg ha-1 yr-1 in Latin America. Genetic trend was, in part, related to the extent of deployment of new breeding tools in each pipeline, strength of an extensive phenotyping network, and funding stability. Over the past decade, CIMMYT's breeding pipelines have significantly evolved, incorporating new tools/technologies to increase selection accuracy and intensity, while reducing cycle time. The first pipeline, Eastern Africa Product Profile 1a (EA-PP1a), to implement marker-assisted forward-breeding for resistance to key diseases, coupled with rapid-cycle genomic selection for drought, recorded a genetic trend of 2.46% per year highlighting the potential for deploying new tools/technologies to increase genetic gain.
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Atanda SA, Govindan V, Singh R, Robbins KR, Crossa J, Bentley AR. Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1939-1950. [PMID: 35348821 PMCID: PMC9205816 DOI: 10.1007/s00122-022-04085-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/16/2022] [Indexed: 06/08/2023]
Abstract
Sparse testing using genomic prediction can be efficiently used to increase the number of testing environments while maintaining selection intensity in the early yield testing stage without increasing the breeding budget. Sparse testing using genomic prediction enables expanded use of selection environments in early-stage yield testing without increasing phenotyping cost. We evaluated different sparse testing strategies in the yield testing stage of a CIMMYT spring wheat breeding pipeline characterized by multiple populations each with small family sizes of 1-9 individuals. Our results indicated that a substantial overlap between lines across environments should be used to achieve optimal prediction accuracy. As sparse testing leverages information generated within and across environments, the genetic correlations between environments and genomic relationships of lines across environments were the main drivers of prediction accuracy in multi-environment yield trials. Including information from previous evaluation years did not consistently improve the prediction performance. Genomic best linear unbiased prediction was found to be the best predictor of true breeding value, and therefore, we propose that it should be used as a selection decision metric in the early yield testing stages. We also propose it as a proxy for assessing prediction performance to mirror breeder's advancement decisions in a breeding program so that it can be readily applied for advancement decisions by breeding programs.
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Affiliation(s)
| | - Velu Govindan
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Ravi Singh
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kelly R Robbins
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, USA
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Alison R Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
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12
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Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2467:77-112. [PMID: 35451773 DOI: 10.1007/978-1-0716-2205-6_3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
The efficiency of genomic selection strongly depends on the prediction accuracy of the genetic merit of candidates. Numerous papers have shown that the composition of the calibration set is a key contributor to prediction accuracy. A poorly defined calibration set can result in low accuracies, whereas an optimized one can considerably increase accuracy compared to random sampling, for a same size. Alternatively, optimizing the calibration set can be a way of decreasing the costs of phenotyping by enabling similar levels of accuracy compared to random sampling but with fewer phenotypic units. We present here the different factors that have to be considered when designing a calibration set, and review the different criteria proposed in the literature. We classified these criteria into two groups: model-free criteria based on relatedness, and criteria derived from the linear mixed model. We introduce criteria targeting specific prediction objectives including the prediction of highly diverse panels, biparental families, or hybrids. We also review different ways of updating the calibration set, and different procedures for optimizing phenotyping experimental designs.
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Martins Oliveira IC, Bernardeli A, Soler Guilhen JH, Pastina MM. Genomic Prediction of Complex Traits in an Allogamous Annual Crop: The Case of Maize Single-Cross Hybrids. Methods Mol Biol 2022; 2467:543-567. [PMID: 35451790 DOI: 10.1007/978-1-0716-2205-6_20] [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] [Indexed: 06/14/2023]
Abstract
For many plant and animal species, commercial products are hybrids between individuals from different genetic groups. For allogamous plant species such as maize, the breeding objective is to produce single-cross hybrid varieties from two inbred lines each selected in complementary groups. Efficient hybrid breeding requires methods that (1) quickly generate homozygous and homogeneous parental lines with high combining abilities, (2) efficiently choose among the large number of available parental lines the most promising ones, and (3) predict the performances of sets of non-phenotyped single-cross hybrids, or hybrids phenotyped in a limited number of environments, based on their relationship with another set of hybrids with known performances. The maize breeding community has been developing model-based prediction of hybrid performances well before the genomic era. This chapter (1) provides a reminder of the maize breeding scheme before the genomic era; (2) describes how genomic data were incorporated in the prediction models involved in different steps of genomic-based single-cross maize hybrid breeding; and (3) reviews factors affecting the accuracy of genomic prediction, approaches for optimizing GP-based single-cross maize hybrid breeding schemes, and ensuring the long-term sustainability of genomic selection.
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Affiliation(s)
| | - Arthur Bernardeli
- Department of Agronomy, Universidade Federal de Viçosa, Viçosa-MG, Brazil
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Crossa J, Montesinos-López OA, Pérez-Rodríguez P, Costa-Neto G, Fritsche-Neto R, Ortiz R, Martini JWR, Lillemo M, Montesinos-López A, Jarquin D, Breseghello F, Cuevas J, Rincent R. Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction. Methods Mol Biol 2022; 2467:245-283. [PMID: 35451779 DOI: 10.1007/978-1-0716-2205-6_9] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Genomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E.
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Affiliation(s)
- José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, Mexico
- Colegio de Postgraduados, Montecillos, Mexico
| | | | | | - Germano Costa-Neto
- Departamento de Genética, Escola Superior de Agricultura "Luiz de Queiroz" (ESALQ/USP), São Paulo, Brazil
| | - Roberto Fritsche-Neto
- Departamento de Genética, Escola Superior de Agricultura "Luiz de Queiroz" (ESALQ/USP), São Paulo, Brazil
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences (SLU), Alnarp, Sweden
| | - Johannes W R Martini
- International Maize and Wheat Improvement Center (CIMMYT), Carretera México-Veracruz, Mexico
| | - Morten Lillemo
- Department of Plant Sciences, Norwegian University of Life Sciences, IHA/CIGENE, Ås, Norway
| | - Abelardo Montesinos-López
- Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, Guadalajara, Jalisco, Mexico
| | | | | | - Jaime Cuevas
- Universidad de Quintana Roo, Chetumal, Quintana Roo, Mexico.
| | - Renaud Rincent
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution - Le Moulon, Gif-sur-Yvette, France.
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Saradadevi GP, Das D, Mangrauthia SK, Mohapatra S, Chikkaputtaiah C, Roorkiwal M, Solanki M, Sundaram RM, Chirravuri NN, Sakhare AS, Kota S, Varshney RK, Mohannath G. Genetic, Epigenetic, Genomic and Microbial Approaches to Enhance Salt Tolerance of Plants: A Comprehensive Review. BIOLOGY 2021; 10:biology10121255. [PMID: 34943170 PMCID: PMC8698797 DOI: 10.3390/biology10121255] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/23/2021] [Accepted: 11/24/2021] [Indexed: 12/17/2022]
Abstract
Simple Summary Globally, soil salinity, which refers to salt-affected soils, is increasing due to various environmental factors and human activities. Soil salinity poses one of the most serious challenges in the field of agriculture as it significantly reduces the growth and yield of crop plants, both quantitatively and qualitatively. Over the last few decades, several studies have been carried out to understand plant biology in response to soil salinity stress with a major emphasis on genetic and other hereditary components. Based on the outcome of these studies, several approaches are being followed to enhance plants’ ability to tolerate salt stress while still maintaining reasonable levels of crop yields. In this manuscript, we comprehensively list and discuss various biological approaches being followed and, based on the recent advances in the field of molecular biology, we propose some new approaches to improve salinity tolerance of crop plants. The global scientific community can make use of this information for the betterment of crop plants. This review also highlights the importance of maintaining global soil health to prevent several crop plant losses. Abstract Globally, soil salinity has been on the rise owing to various factors that are both human and environmental. The abiotic stress caused by soil salinity has become one of the most damaging abiotic stresses faced by crop plants, resulting in significant yield losses. Salt stress induces physiological and morphological modifications in plants as a result of significant changes in gene expression patterns and signal transduction cascades. In this comprehensive review, with a major focus on recent advances in the field of plant molecular biology, we discuss several approaches to enhance salinity tolerance in plants comprising various classical and advanced genetic and genetic engineering approaches, genomics and genome editing technologies, and plant growth-promoting rhizobacteria (PGPR)-based approaches. Furthermore, based on recent advances in the field of epigenetics, we propose novel approaches to create and exploit heritable genome-wide epigenetic variation in crop plants to enhance salinity tolerance. Specifically, we describe the concepts and the underlying principles of epigenetic recombinant inbred lines (epiRILs) and other epigenetic variants and methods to generate them. The proposed epigenetic approaches also have the potential to create additional genetic variation by modulating meiotic crossover frequency.
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Affiliation(s)
- Gargi Prasad Saradadevi
- Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad 500078, India; (G.P.S.); (S.M.)
| | - Debajit Das
- Biological Sciences and Technology Division, CSIR-North East Institute of Science and Technology (CSIR-NEIST), Jorhat 785006, India; (D.D.); (C.C.)
| | - Satendra K. Mangrauthia
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Sridev Mohapatra
- Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad 500078, India; (G.P.S.); (S.M.)
| | - Channakeshavaiah Chikkaputtaiah
- Biological Sciences and Technology Division, CSIR-North East Institute of Science and Technology (CSIR-NEIST), Jorhat 785006, India; (D.D.); (C.C.)
| | - Manish Roorkiwal
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India;
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
| | - Manish Solanki
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Raman Meenakshi Sundaram
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Neeraja N. Chirravuri
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Akshay S. Sakhare
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Suneetha Kota
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
- Correspondence: (S.K.); (R.K.V.); (G.M.); Tel.: +91-40-245-91268 (S.K.); +91-84-556-83305 (R.K.V.); +91-40-66303697 (G.M.)
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India;
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, WA 6150, Australia
- Correspondence: (S.K.); (R.K.V.); (G.M.); Tel.: +91-40-245-91268 (S.K.); +91-84-556-83305 (R.K.V.); +91-40-66303697 (G.M.)
| | - Gireesha Mohannath
- Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad 500078, India; (G.P.S.); (S.M.)
- Correspondence: (S.K.); (R.K.V.); (G.M.); Tel.: +91-40-245-91268 (S.K.); +91-84-556-83305 (R.K.V.); +91-40-66303697 (G.M.)
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Gholami M, Wimmer V, Sansaloni C, Petroli C, Hearne SJ, Covarrubias-Pazaran G, Rensing S, Heise J, Pérez-Rodríguez P, Dreisigacker S, Crossa J, Martini JWR. A Comparison of the Adoption of Genomic Selection Across Different Breeding Institutions. FRONTIERS IN PLANT SCIENCE 2021; 12:728567. [PMID: 34868114 PMCID: PMC8640095 DOI: 10.3389/fpls.2021.728567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 09/30/2021] [Indexed: 06/13/2023]
Affiliation(s)
| | | | - Carolina Sansaloni
- Genetic Resources Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
| | - Cesar Petroli
- Genetic Resources Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
| | - Sarah J. Hearne
- Genetic Resources Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
- Excellence in Breeding Platform, Consultative Group for International Agricultural Research, Texcoco, Mexico
| | | | - Stefan Rensing
- IT Solutions for Animal Production (vit - Vereinigte Informationssysteme Tierhaltung w.V.), Verden, Germany
| | - Johannes Heise
- IT Solutions for Animal Production (vit - Vereinigte Informationssysteme Tierhaltung w.V.), Verden, Germany
| | | | - Susanne Dreisigacker
- Global Wheat Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
| | - José Crossa
- Genetic Resources Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
- Department of Statistics, Colegio de Postgraduados, Montecillos, Mexico
| | - Johannes W. R. Martini
- Genetic Resources Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
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17
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Lopez-Cruz M, Beyene Y, Gowda M, Crossa J, Pérez-Rodríguez P, de los Campos G. Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices. Heredity (Edinb) 2021; 127:423-432. [PMID: 34564692 PMCID: PMC8551287 DOI: 10.1038/s41437-021-00474-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 09/10/2021] [Accepted: 09/11/2021] [Indexed: 02/07/2023] Open
Abstract
Genomic prediction models are often calibrated using multi-generation data. Over time, as data accumulates, training data sets become increasingly heterogeneous. Differences in allele frequency and linkage disequilibrium patterns between the training and prediction genotypes may limit prediction accuracy. This leads to the question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Previous research on training set optimization has focused on identifying a subset of the available data that is optimal for a given prediction set. However, this approach does not contemplate the possibility that different training sets may be optimal for different prediction genotypes. To address this problem, we recently introduced a sparse selection index (SSI) that identifies an optimal training set for each individual in a prediction set. Using additive genomic relationships, the SSI can provide increased accuracy relative to genomic-BLUP (GBLUP). Non-parametric genomic models using Gaussian kernels (KBLUP) have, in some cases, yielded higher prediction accuracies than standard additive models. Therefore, here we studied whether combining SSIs and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. Using four years of doubled haploid maize data from the International Maize and Wheat Improvement Center (CIMMYT), we found that when predicting grain yield the KBLUP outperformed the GBLUP, and that using SSI with additive relationships (GSSI) lead to 5-17% increases in accuracy, relative to the GBLUP. However, differences in prediction accuracy between the KBLUP and the kernel-based SSI were smaller and not always significant.
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Affiliation(s)
- Marco Lopez-Cruz
- grid.17088.360000 0001 2150 1785Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI USA ,grid.17088.360000 0001 2150 1785Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI USA
| | - Yoseph Beyene
- grid.512317.30000 0004 7645 1801Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Manje Gowda
- grid.512317.30000 0004 7645 1801Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Jose Crossa
- grid.433436.50000 0001 2289 885XBiometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Mexico, Mexico ,grid.418752.d0000 0004 1795 9752Colegio de Postgraduados, Montecillos, Edo. de México, Mexico
| | - Paulino Pérez-Rodríguez
- grid.418752.d0000 0004 1795 9752Colegio de Postgraduados, Montecillos, Edo. de México, Mexico
| | - Gustavo de los Campos
- grid.17088.360000 0001 2150 1785Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI USA ,grid.17088.360000 0001 2150 1785Department of Statistics and Probability, Michigan State University, East Lansing, MI USA ,grid.17088.360000 0001 2150 1785Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI USA
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Rembe M, Reif JC, Ebmeyer E, Thorwarth P, Korzun V, Schacht J, Boeven PHG, Varenne P, Kazman E, Philipp N, Kollers S, Pfeiffer N, Longin CFH, Hartwig N, Gils M, Zhao Y. Reciprocal Recurrent Genomic Selection Is Impacted by Genotype-by-Environment Interactions. FRONTIERS IN PLANT SCIENCE 2021; 12:703419. [PMID: 34630453 PMCID: PMC8498042 DOI: 10.3389/fpls.2021.703419] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 08/02/2021] [Indexed: 06/13/2023]
Abstract
Reciprocal recurrent genomic selection is a breeding strategy aimed at improving the hybrid performance of two base populations. It promises to significantly advance hybrid breeding in wheat. Against this backdrop, the main objective of this study was to empirically investigate the potential and limitations of reciprocal recurrent genomic selection. Genome-wide predictive equations were developed using genomic and phenotypic data from a comprehensive population of 1,604 single crosses between 120 female and 15 male wheat lines. Twenty superior female lines were selected for initiation of the reciprocal recurrent genomic selection program. Focusing on the female pool, one cycle was performed with genomic selection steps at the F2 (60 out of 629 plants) and the F5 stage (49 out of 382 plants). Selection gain for grain yield was evaluated at six locations. Analyses of the phenotypic data showed pronounced genotype-by-environment interactions with two environments that formed an outgroup compared to the environments used for the genome-wide prediction equations. Removing these two environments for further analysis resulted in a selection gain of 1.0 dt ha-1 compared to the hybrids of the original 20 parental lines. This underscores the potential of reciprocal recurrent genomic selection to promote hybrid wheat breeding, but also highlights the need to develop robust genome-wide predictive equations.
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Affiliation(s)
- Maximilian Rembe
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany
| | | | | | | | - Viktor Korzun
- KWS SAAT SE & Co. KGaA, Einbeck, Germany
- Federal State Budgetary Institution of Science Federal Research Center “Kazan Scientific Center of Russian Academy of Sciences”, Kazan, Russia
| | - Johannes Schacht
- Limagrain Europe, Ferme de l'Etang – BP3−77390, Verneuil-l'Ètang, France
| | | | - Pierrick Varenne
- Limagrain Europe, Ferme de l'Etang – BP3−77390, Verneuil-l'Ètang, France
| | | | | | | | | | | | | | - Mario Gils
- Nordsaat Saatzucht GmbH, Langenstein, Germany
| | - Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany
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19
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Isidro y Sánchez J, Akdemir D. Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview. FRONTIERS IN PLANT SCIENCE 2021; 12:715910. [PMID: 34589099 PMCID: PMC8475495 DOI: 10.3389/fpls.2021.715910] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/10/2021] [Indexed: 06/13/2023]
Abstract
Genomic selection (GS) is becoming an essential tool in breeding programs due to its role in increasing genetic gain per unit time. The design of the training set (TRS) in GS is one of the key steps in the implementation of GS in plant and animal breeding programs mainly because (i) TRS optimization is critical for the efficiency and effectiveness of GS, (ii) breeders test genotypes in multi-year and multi-location trials to select the best-performing ones. In this framework, TRS optimization can help to decrease the number of genotypes to be tested and, therefore, reduce phenotyping cost and time, and (iii) we can obtain better prediction accuracies from optimally selected TRS than an arbitrary TRS. Here, we concentrate the efforts on reviewing the lessons learned from TRS optimization studies and their impact on crop breeding and discuss important features for the success of TRS optimization under different scenarios. In this article, we review the lessons learned from training population optimization in plants and the major challenges associated with the optimization of GS including population size, the relationship between training and test set (TS), update of TRS, and the use of different packages and algorithms for TRS implementation in GS. Finally, we describe general guidelines to improving the rate of genetic improvement by maximizing the use of the TRS optimization in the GS framework.
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Affiliation(s)
- Julio Isidro y Sánchez
- Centro de Biotecnologia y Genómica de Plantas, Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria, Universidad Politécnica de Madrid, Campus de Montegancedo, Madrid, Spain
| | - Deniz Akdemir
- Animal and Crop Science Division, Agriculture and Food Science Centre, University College Dublin, Dublin, Ireland
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20
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Cao S, Song J, Yuan Y, Zhang A, Ren J, Liu Y, Qu J, Hu G, Zhang J, Wang C, Cao J, Olsen M, Prasanna BM, San Vicente F, Zhang X. Genomic Prediction of Resistance to Tar Spot Complex of Maize in Multiple Populations Using Genotyping-by-Sequencing SNPs. FRONTIERS IN PLANT SCIENCE 2021; 12:672525. [PMID: 34335648 PMCID: PMC8322742 DOI: 10.3389/fpls.2021.672525] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 06/21/2021] [Indexed: 06/13/2023]
Abstract
Tar spot complex (TSC) is one of the most important foliar diseases in tropical maize. TSC resistance could be furtherly improved by implementing marker-assisted selection (MAS) and genomic selection (GS) individually, or by implementing them stepwise. Implementation of GS requires a profound understanding of factors affecting genomic prediction accuracy. In the present study, an association-mapping panel and three doubled haploid populations, genotyped with genotyping-by-sequencing, were used to estimate the effectiveness of GS for improving TSC resistance. When the training and prediction sets were independent, moderate-to-high prediction accuracies were achieved across populations by using the training sets with broader genetic diversity, or in pairwise populations having closer genetic relationships. A collection of inbred lines with broader genetic diversity could be used as a permanent training set for TSC improvement, which can be updated by adding more phenotyped lines having closer genetic relationships with the prediction set. The prediction accuracies estimated with a few significantly associated SNPs were moderate-to-high, and continuously increased as more significantly associated SNPs were included. It confirmed that TSC resistance could be furtherly improved by implementing GS for selecting multiple stable genomic regions simultaneously, or by implementing MAS and GS stepwise. The factors of marker density, marker quality, and heterozygosity rate of samples had minor effects on the estimation of the genomic prediction accuracy. The training set size, the genetic relationship between training and prediction sets, phenotypic and genotypic diversity of the training sets, and incorporating known trait-marker associations played more important roles in improving prediction accuracy. The result of the present study provides insight into less complex trait improvement via GS in maize.
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Affiliation(s)
- Shiliang Cao
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
| | - Junqiao Song
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- College of Agronomy, Henan University of Science and Technology, Luoyang, China
- Maize Research Institute, Anyang Academy of Agricultural Sciences, Anyang, China
| | - Yibing Yuan
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Ao Zhang
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- College of Biological Science and Technology, Shenyang Agricultural University, Shenyang, China
| | - Jiaojiao Ren
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- College of Agronomy, Xinjiang Agricultural University, Urumqi, China
| | - Yubo Liu
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- College of Biological Science and Technology, Shenyang Agricultural University, Shenyang, China
| | - Jingtao Qu
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Guanghui Hu
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
| | - Jianguo Zhang
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Chunping Wang
- Maize Research Institute, Anyang Academy of Agricultural Sciences, Anyang, China
| | - Jingsheng Cao
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Michael Olsen
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | | | - Felix San Vicente
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
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21
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Fritsche-Neto R, Galli G, Borges KLR, Costa-Neto G, Alves FC, Sabadin F, Lyra DH, Morais PPP, Braatz de Andrade LR, Granato I, Crossa J. Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review. FRONTIERS IN PLANT SCIENCE 2021; 12:658267. [PMID: 34276721 PMCID: PMC8281958 DOI: 10.3389/fpls.2021.658267] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 05/10/2021] [Indexed: 06/13/2023]
Abstract
The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of São Paulo (USP, Brazil) and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotype-environment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions.
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Affiliation(s)
- Roberto Fritsche-Neto
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Giovanni Galli
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Karina Lima Reis Borges
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Germano Costa-Neto
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Filipe Couto Alves
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, United States
| | - Felipe Sabadin
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Danilo Hottis Lyra
- Department of Computational and Analytical Sciences, Rothamsted Research, Harpenden, United Kingdom
| | | | | | - Italo Granato
- Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA), Univ. Montpellier, SupAgro, Montpellier, France
| | - Jose Crossa
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Carretera México - Veracruz, Texcoco, Mexico
- Colegio de Posgraduado, Montecillo, Mexico
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22
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Beyene Y, Gowda M, Pérez-Rodríguez P, Olsen M, Robbins KR, Burgueño J, Prasanna BM, Crossa J. Application of Genomic Selection at the Early Stage of Breeding Pipeline in Tropical Maize. FRONTIERS IN PLANT SCIENCE 2021; 12:685488. [PMID: 34262585 PMCID: PMC8274566 DOI: 10.3389/fpls.2021.685488] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 05/31/2021] [Indexed: 06/13/2023]
Abstract
In maize, doubled haploid (DH) line production capacity of large-sized maize breeding programs often exceeds the capacity to phenotypically evaluate the complete set of testcross candidates in multi-location trials. The ability to partially select DH lines based on genotypic data while maintaining or improving genetic gains for key traits using phenotypic selection can result in significant resource savings. The present study aimed to evaluate genomic selection (GS) prediction scenarios for grain yield and agronomic traits of one of the tropical maize breeding pipelines of CIMMYT in eastern Africa, based on multi-year empirical data for designing a GS-based strategy at the early stages of the pipeline. We used field data from 3,068 tropical maize DH lines genotyped using rAmpSeq markers and evaluated as test crosses in well-watered (WW) and water-stress (WS) environments in Kenya from 2017 to 2019. Three prediction schemes were compared: (1) 1 year of performance data to predict a second year; (2) 2 years of pooled data to predict performance in the third year, and (3) using individual or pooled data plus converting a certain proportion of individuals from the testing set (TST) to the training set (TRN) to predict the next year's data. Employing five-fold cross-validation, the mean prediction accuracies for grain yield (GY) varied from 0.19 to 0.29 under WW and 0.22 to 0.31 under WS, when the 1-year datasets were used training set to predict a second year's data as a testing set. The mean prediction accuracies increased to 0.32 under WW and 0.31 under WS when the 2-year datasets were used as a training set to predict the third-year data set. In a forward prediction scenario, good predictive abilities (0.53 to 0.71) were found when the training set consisted of the previous year's breeding data and converting 30% of the next year's data from the testing set to the training set. The prediction accuracy for anthesis date and plant height across WW and WS environments obtained using 1-year data and integrating 10, 30, 50, 70, and 90% of the TST set to TRN set was much higher than those trained in individual years. We demonstrate that by increasing the TRN set to include genotypic and phenotypic data from the previous year and combining only 10-30% of the lines from the year of testing, the predicting accuracy can be increased, which in turn could be used to replace the first stage of field-based screening partially, thus saving significant costs associated with the testcross formation and multi-location testcross evaluation.
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Affiliation(s)
- Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | | | - Michael Olsen
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Kelly R. Robbins
- School of Integrative Plant Science-Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, United States
| | - Juan Burgueño
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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23
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Atanda SA, Olsen M, Crossa J, Burgueño J, Rincent R, Dzidzienyo D, Beyene Y, Gowda M, Dreher K, Boddupalli PM, Tongoona P, Danquah EY, Olaoye G, Robbins KR. Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage. FRONTIERS IN PLANT SCIENCE 2021; 12:658978. [PMID: 34239521 PMCID: PMC8259603 DOI: 10.3389/fpls.2021.658978] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 05/25/2021] [Indexed: 06/08/2023]
Abstract
To enable a scalable sparse testing genomic selection (GS) strategy at preliminary yield trials in the CIMMYT maize breeding program, optimal approaches to incorporate genotype by environment interaction (GEI) in genomic prediction models are explored. Two cross-validation schemes were evaluated: CV1, predicting the genetic merit of new bi-parental populations that have been evaluated in some environments and not others, and CV2, predicting the genetic merit of half of a bi-parental population that has been phenotyped in some environments and not others using the coefficient of determination (CDmean) to determine optimized subsets of a full-sib family to be evaluated in each environment. We report similar prediction accuracies in CV1 and CV2, however, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, allowing efficient use of information across environments. It is also ideal for building robust historical data because all individuals of a full-sib family have phenotypic data, albeit in different environments. Results show that grouping of environments according to similar growing/management conditions improved prediction accuracy and reduced computational requirements, providing a scalable, parsimonious approach to multi-environmental trials and GS in early testing stages. We further demonstrate that complementing the full-sib calibration set with optimized historical data results in improved prediction accuracy for the cross-validation schemes.
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Affiliation(s)
- Sikiru Adeniyi Atanda
- West Africa Center for Crop Improvement (WACCI), University of Ghana, Accra, Ghana
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
| | - Michael Olsen
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Juan Burgueño
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Renaud Rincent
- French National Institute for Agriculture, Food, and Environment (INRAE), Paris, France
| | - Daniel Dzidzienyo
- West Africa Center for Crop Improvement (WACCI), University of Ghana, Accra, Ghana
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Kate Dreher
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Pangirayi Tongoona
- West Africa Center for Crop Improvement (WACCI), University of Ghana, Accra, Ghana
| | | | - Gbadebo Olaoye
- Agronomy Department, University of Ilorin, Ilorin, Nigeria
| | - Kelly R. Robbins
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
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