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Tessema BB, Raffo MA, Guo X, Svane SF, Krusell L, Jensen JD, Ruud AK, Malinowska M, Thorup-Kristensen K, Jensen J. Genomic prediction for root and yield traits of barley under a water availability gradient: a case study comparing different spatial adjustments. PLANT METHODS 2024; 20:8. [PMID: 38216953 PMCID: PMC10785381 DOI: 10.1186/s13007-023-01121-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 12/04/2023] [Indexed: 01/14/2024]
Abstract
BACKGROUND In drought periods, water use efficiency depends on the capacity of roots to extract water from deep soil. A semi-field phenotyping facility (RadiMax) was used to investigate above-ground and root traits in spring barley when grown under a water availability gradient. Above-ground traits included grain yield, grain protein concentration, grain nitrogen removal, and thousand kernel weight. Root traits were obtained through digital images measuring the root length at different depths. Two nearest-neighbor adjustments (M1 and M2) to model spatial variation were used for genetic parameter estimation and genomic prediction (GP). M1 and M2 used (co)variance structures and differed in the distance function to calculate between-neighbor correlations. M2 was the most developed adjustment, as accounted by the Euclidean distance between neighbors. RESULTS The estimated heritabilities ([Formula: see text]) ranged from low to medium for root and above-ground traits. The genetic coefficient of variation ([Formula: see text]) ranged from 3.2 to 7.0% for above-ground and 4.7 to 10.4% for root traits, indicating good breeding potential for the measured traits. The highest [Formula: see text] observed for root traits revealed that significant genetic change in root development can be achieved through selection. We studied the genotype-by-water availability interaction, but no relevant interaction effects were detected. GP was assessed using leave-one-line-out (LOO) cross-validation. The predictive ability (PA) estimated as the correlation between phenotypes corrected by fixed effects and genomic estimated breeding values ranged from 0.33 to 0.49 for above-ground and 0.15 to 0.27 for root traits, and no substantial variance inflation in predicted genetic effects was observed. Significant differences in PA were observed in favor of M2. CONCLUSIONS The significant [Formula: see text] and the accurate prediction of breeding values for above-ground and root traits revealed that developing genetically superior barley lines with improved root systems is possible. In addition, we found significant spatial variation in the experiment, highlighting the relevance of correctly accounting for spatial effects in statistical models. In this sense, the proposed nearest-neighbor adjustments are flexible approaches in terms of assumptions that can be useful for semi-field or field experiments.
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Affiliation(s)
- Biructawit B Tessema
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, USA.
| | - Miguel A Raffo
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark.
| | - Xiangyu Guo
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
- Danish Pig Research Centre, Danish Agriculture & Food Council, Copenhagen, Denmark
| | - Simon F Svane
- Department of Plant and Environmental Science, University of Copenhagen, 1871, Frederiksberg, Denmark
| | - Lene Krusell
- Sejet Plant Breeding I/S, 8700, Horsens, Denmark
| | | | - Anja Karine Ruud
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
- Faculty of Biosciences, Department of Plant Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Marta Malinowska
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
| | | | - Just Jensen
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830, Tjele, Denmark
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2
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Abebe AM, Kim Y, Kim J, Kim SL, Baek J. Image-Based High-Throughput Phenotyping in Horticultural Crops. PLANTS (BASEL, SWITZERLAND) 2023; 12:2061. [PMID: 37653978 PMCID: PMC10222289 DOI: 10.3390/plants12102061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/12/2023] [Accepted: 05/18/2023] [Indexed: 09/02/2023]
Abstract
Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.
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Affiliation(s)
| | | | | | | | - Jeongho Baek
- Department of Agricultural Biotechnology, National Institute of Agricultural Science, Rural Development Administration, Jeonju 54874, Republic of Korea
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3
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Cassava root crown phenotyping using three-dimension (3D) multi-view stereo reconstruction. Sci Rep 2022; 12:10030. [PMID: 35705690 PMCID: PMC9200979 DOI: 10.1038/s41598-022-14325-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 06/06/2022] [Indexed: 11/09/2022] Open
Abstract
Phenotypic analysis of cassava root crowns (CRCs) so far has been limited to visual inspection and very few measurements due to its laborious process in the field. Here, we developed a platform for acquiring 3D CRC models using close-range photogrammetry for phenotypic analysis. The state of the art is a low cost and easy to set up 3D acquisition requiring only a background sheet, a reference object and a camera, compatible with field experiments in remote areas. We tested different software with CRC samples, and Agisoft and Blender were the most suitable software for generating high-quality 3D models and data analysis, respectively. We optimized the workflow by testing different numbers of images for 3D reconstruction and found that a minimum of 25 images per CRC can provide high quality 3D models. Up to ten traits, including 3D crown volumes, 3D crown surface, root density, surface-to-volume ratio, root numbers, root angle, crown diameter, cylinder soil volume, CRC compactness and root length can be extracted providing novel parameters for studying cassava storage roots. We applied this platform to partial-inbred cassava populations and demonstrated that our platform provides reliable 3D CRC modelling for phenotypic analysis, analysis of genetic variances and supporting breeding selection.
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Phumichai C, Aiemnaka P, Nathaisong P, Hunsawattanakul S, Fungfoo P, Rojanaridpiched C, Vichukit V, Kongsil P, Kittipadakul P, Wannarat W, Chunwongse J, Tongyoo P, Kijkhunasatian C, Chotineeranat S, Piyachomkwan K, Wolfe MD, Jannink JL, Sorrells ME. Genome-wide association mapping and genomic prediction of yield-related traits and starch pasting properties in cassava. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:145-171. [PMID: 34661695 DOI: 10.1007/s00122-021-03956-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 09/25/2021] [Indexed: 06/13/2023]
Abstract
GWAS identified eight yield-related, peak starch type of waxy and wild-type starch and 21 starch pasting property-related traits (QTLs). Prediction ability of eight GS models resulted in low to high predictability, depending on trait, heritability, and genetic architecture. Cassava is both a food and an industrial crop in Africa, South America, and Asia, but knowledge of the genes that control yield and starch pasting properties remains limited. We carried out a genome-wide association study to clarify the molecular mechanisms underlying these traits and to explore marker-based breeding approaches. We estimated the predictive ability of genomic selection (GS) using parametric, semi-parametric, and nonparametric GS models with a panel of 276 cassava genotypes from Thai Tapioca Development Institute, International Center for Tropical Agriculture, International Institute of Tropical Agriculture, and other breeding programs. The cassava panel was genotyped via genotyping-by-sequencing, and 89,934 single-nucleotide polymorphism (SNP) markers were identified. A total of 31 SNPs associated with yield, starch type, and starch properties traits were detected by the fixed and random model circulating probability unification (FarmCPU), Bayesian-information and linkage-disequilibrium iteratively nested keyway and compressed mixed linear model, respectively. GS models were developed, and forward predictabilities using all the prediction methods resulted in values of - 0.001-0.71 for the four yield-related traits and 0.33-0.82 for the seven starch pasting property traits. This study provides additional insight into the genetic architecture of these important traits for the development of markers that could be used in cassava breeding programs.
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Affiliation(s)
- Chalermpol Phumichai
- Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand.
- Center for Agricultural Biotechnology, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom, 73140, Thailand.
- Center of Excellence On Agricultural Biotechnology: (AG-BIO/MHESI), Bangkok, 10900, Thailand.
| | - Pornsak Aiemnaka
- Thai Tapioca Development Institute, Lumpini Tower, 1168/26 Rama IV Road, Bangkok, 10120, Thailand
| | - Piyaporn Nathaisong
- Thai Tapioca Development Institute, Lumpini Tower, 1168/26 Rama IV Road, Bangkok, 10120, Thailand
| | - Sirikan Hunsawattanakul
- Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand
- Center for Agricultural Biotechnology, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom, 73140, Thailand
- Center of Excellence On Agricultural Biotechnology: (AG-BIO/MHESI), Bangkok, 10900, Thailand
| | - Phasakorn Fungfoo
- Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand
| | | | - Vichan Vichukit
- Thai Tapioca Development Institute, Lumpini Tower, 1168/26 Rama IV Road, Bangkok, 10120, Thailand
| | - Pasajee Kongsil
- Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand
| | - Piya Kittipadakul
- Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand
| | - Wannasiri Wannarat
- Department of Agronomy, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand
| | - Julapark Chunwongse
- Department of Horticulture, Faculty of Agriculture Kamphaeng Saen, Kasetsart University, Nakhon Pathom, 73140, Thailand
| | - Pumipat Tongyoo
- Center for Agricultural Biotechnology, Kasetsart University, Kamphaeng Saen Campus, Nakhon Pathom, 73140, Thailand
| | - Chookiat Kijkhunasatian
- Cassava and Starch Technology Research Team, National Center for Genetic Engineering and Biotechnology, Pathumthani, 12120, Thailand
| | - Sunee Chotineeranat
- Cassava and Starch Technology Research Team, National Center for Genetic Engineering and Biotechnology, Pathumthani, 12120, Thailand
| | - Kuakoon Piyachomkwan
- Cassava and Starch Technology Research Team, National Center for Genetic Engineering and Biotechnology, Pathumthani, 12120, Thailand
| | - Marnin D Wolfe
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, 14850, USA
| | - Jean-Luc Jannink
- United States Department of Agriculture - Agriculture Research Service, Ithaca, NY, 14850, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, Cornell University, Ithaca, NY, 14850, USA
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Torres LG, de Oliveira EJ, Ogbonna AC, Bauchet GJ, Mueller LA, Azevedo CF, Fonseca e Silva F, Simiqueli GF, de Resende MDV. Can Cross-Country Genomic Predictions Be a Reasonable Strategy to Support Germplasm Exchange? - A Case Study With Hydrogen Cyanide in Cassava. FRONTIERS IN PLANT SCIENCE 2021; 12:742638. [PMID: 34956254 PMCID: PMC8692580 DOI: 10.3389/fpls.2021.742638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 11/08/2021] [Indexed: 06/14/2023]
Abstract
Genomic prediction (GP) offers great opportunities for accelerated genetic gains by optimizing the breeding pipeline. One of the key factors to be considered is how the training populations (TP) are composed in terms of genetic improvement, kinship/origin, and their impacts on GP. Hydrogen cyanide content (HCN) is a determinant trait to guide cassava's products usage and processing. This work aimed to achieve the following objectives: (i) evaluate the feasibility of using cross-country (CC) GP between germplasm's of Embrapa Mandioca e Fruticultura (Embrapa, Brazil) and The International Institute of Tropical Agriculture (IITA, Nigeria) for HCN; (ii) provide an assessment of population structure for the joint dataset; (iii) estimate the genetic parameters based on single nucleotide polymorphisms (SNPs) and a haplotype-approach. Datasets of HCN from Embrapa and IITA breeding programs were analyzed, separately and jointly, with 1,230, 590, and 1,820 clones, respectively. After quality control, ∼14K SNPs were used for GP. The genomic estimated breeding values (GEBVs) were predicted based on SNP effects from analyses with TP composed of the following: (i) Embrapa genotypic and phenotypic data, (ii) IITA genotypic and phenotypic data, and (iii) the joint datasets. Comparisons on GEBVs' estimation were made considering the hypothetical situation of not having the phenotypic characterization for a set of clones for a certain research institute/country and might need to use the markers' effects that were trained with data from other research institutes/country's germplasm to estimate their clones' GEBV. Fixation index (FST) among the genetic groups identified within the joint dataset ranged from 0.002 to 0.091. The joint dataset provided an improved accuracy (0.8-0.85) compared to the prediction accuracy of either germplasm's sources individually (0.51-0.67). CC GP proved to have potential use under the present study's scenario, the correlation between GEBVs predicted with TP from Embrapa and IITA was 0.55 for Embrapa's germplasm, whereas for IITA's it was 0.1. This seems to be among the first attempts to evaluate the CC GP in plants. As such, a lot of useful new information was provided on the subject, which can guide new research on this very important and emerging field.
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Affiliation(s)
- Lívia Gomes Torres
- Department of Plant Science, Universidade Federal de Viçosa, Viçosa, Brazil
| | | | - Alex C. Ogbonna
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
- Boyce Thompson Institute, Ithaca, NY, United States
| | | | - Lukas A. Mueller
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY, United States
- Boyce Thompson Institute, Ithaca, NY, United States
| | | | | | | | - Marcos Deon Vilela de Resende
- Department of Forestry Engineering, Universidade Federal de Viçosa, Viçosa, Brazil
- Embrapa Café, Universidade Federal de Viçosa, Viçosa, Brazil
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6
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Wolfe MD, Chan AW, Kulakow P, Rabbi I, Jannink JL. Genomic mating in outbred species: predicting cross usefulness with additive and total genetic covariance matrices. Genetics 2021; 219:iyab122. [PMID: 34740244 PMCID: PMC8570794 DOI: 10.1093/genetics/iyab122] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 07/13/2021] [Indexed: 11/14/2022] Open
Abstract
Diverse crops are both outbred and clonally propagated. Breeders typically use truncation selection of parents and invest significant time, land, and money evaluating the progeny of crosses to find exceptional genotypes. We developed and tested genomic mate selection criteria suitable for organisms of arbitrary homozygosity level where the full-sibling progeny are of direct interest as future parents and/or cultivars. We extended cross variance and covariance variance prediction to include dominance effects and predicted the multivariate selection index genetic variance of crosses based on haplotypes of proposed parents, marker effects, and recombination frequencies. We combined the predicted mean and variance into usefulness criteria for parent and variety development. We present an empirical study of cassava (Manihot esculenta), a staple tropical root crop. We assessed the potential to predict the multivariate genetic distribution (means, variances, and trait covariances) of 462 cassava families in terms of additive and total value using cross-validation. Most variance (89%) and covariance (70%) prediction accuracy estimates were greater than zero. The usefulness of crosses was accurately predicted with good correspondence between the predicted and the actual mean performance of family members breeders selected for advancement as new parents and candidate varieties. We also used a directional dominance model to quantify significant inbreeding depression for most traits. We predicted 47,083 possible crosses of 306 parents and contrasted them to those previously tested to show how mate selection can reveal the new potential within the germplasm. We enable breeders to consider the potential of crosses to produce future parents (progeny with top breeding values) and varieties (progeny with top own performance).
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Affiliation(s)
- Marnin D Wolfe
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences,
Cornell University, Ithaca, NY 14850, USA
| | - Ariel W Chan
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences,
Cornell University, Ithaca, NY 14850, USA
| | - Peter Kulakow
- International Institute of Tropical Agriculture (IITA), Ibadan,
Nigeria
| | - Ismail Rabbi
- International Institute of Tropical Agriculture (IITA), Ibadan,
Nigeria
| | - Jean-Luc Jannink
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences,
Cornell University, Ithaca, NY 14850, USA
- USDA-ARS, Ithaca, NY 14850, USA
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7
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Yassue RM, Carvalho HF, Gevartosky R, Sabadin F, Souza PH, Bonatelli ML, Azevedo JL, Quecine MC, Fritsche-Neto R. On the genetic architecture in a public tropical maize panel of the symbiosis between corn and plant growth-promoting bacteria aiming to improve plant resilience. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:63. [PMID: 37309313 PMCID: PMC10236062 DOI: 10.1007/s11032-021-01257-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 09/30/2021] [Indexed: 06/14/2023]
Abstract
Exploring the symbiosis between plants and plant growth-promoting bacteria (PGPB) is a new challenge for sustainable agriculture. Even though many works have reported the beneficial effects of PGPB in increasing plant resilience for several stresses, its potential is not yet widely explored. One of the many reasons is the differential symbiosis performance depending on the host genotype. This opens doors to plant breeding programs to explore the genetic variability and develop new cultivars with higher responses to PGPB interaction and, therefore, have higher resilience to stress. Hence, we aimed to study the genetic architecture of the symbiosis between PGPB and tropical maize germplasm, using a public association panel and its impact on plant resilience. Our findings reveal that the synthetic PGPB population can modulate and impact root architecture traits and improve resilience to nitrogen stress, and 37 regions were significant for controlling the symbiosis between PGPB and tropical maize. In addition, we found two overlapping SNPs in the GWAS analysis indicating strong candidates for further investigations. Furthermore, genomic prediction analysis with genomic relationship matrix computed using only significant SNPs obtained from GWAS analysis substantially increased the predictive ability for several traits endorsing the importance of these genomic regions for the response of PGPB. Finally, the public tropical panel reveals a significant genetic variability to the symbiosis with the PGPB and can be a source of alleles to improve plant resilience. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-021-01257-6.
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Affiliation(s)
- Rafael Massahiro Yassue
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, São Paulo Brazil
| | - Humberto Fanelli Carvalho
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, São Paulo Brazil
| | - Raysa Gevartosky
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, São Paulo Brazil
| | - Felipe Sabadin
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, São Paulo Brazil
| | - Pedro Henrique Souza
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, São Paulo Brazil
| | - Maria Leticia Bonatelli
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, São Paulo Brazil
| | - João Lúcio Azevedo
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, São Paulo Brazil
| | - Maria Carolina Quecine
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, São Paulo Brazil
| | - Roberto Fritsche-Neto
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, São Paulo Brazil
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Mbanjo EGN, Rabbi IY, Ferguson ME, Kayondo SI, Eng NH, Tripathi L, Kulakow P, Egesi C. Technological Innovations for Improving Cassava Production in Sub-Saharan Africa. Front Genet 2021; 11:623736. [PMID: 33552138 PMCID: PMC7859516 DOI: 10.3389/fgene.2020.623736] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 12/23/2020] [Indexed: 11/17/2022] Open
Abstract
Cassava is crucial for food security of millions of people in sub-Saharan Africa. The crop has great potential to contribute to African development and is increasing its income-earning potential for small-scale farmers and related value chains on the continent. Therefore, it is critical to increase cassava production, as well as its quality attributes. Technological innovations offer great potential to drive this envisioned change. This paper highlights genomic tools and resources available in cassava. The paper also provides a glimpse of how these resources have been used to screen and understand the pattern of cassava genetic diversity on the continent. Here, we reviewed the approaches currently used for phenotyping cassava traits, highlighting the methodologies used to link genotypic and phenotypic information, dissect the genetics architecture of key cassava traits, and identify quantitative trait loci/markers significantly associated with those traits. Additionally, we examined how knowledge acquired is utilized to contribute to crop improvement. We explored major approaches applied in the field of molecular breeding for cassava, their promises, and limitations. We also examined the role of national agricultural research systems as key partners for sustainable cassava production.
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Affiliation(s)
| | | | | | | | - Ng Hwa Eng
- CGIAR Excellence in Breeding Platform, El Batan, Mexico
| | - Leena Tripathi
- International Institute of Tropical Agriculture, Nairobi, Kenya
| | - Peter Kulakow
- International Institute of Tropical Agriculture, Ibadan, Nigeria
| | - Chiedozie Egesi
- International Institute of Tropical Agriculture, Ibadan, Nigeria
- National Root Crops Research Institute, Umudike, Nigeria
- Department of Global Development, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States
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9
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Genomic Selection for Forest Tree Improvement: Methods, Achievements and Perspectives. FORESTS 2020. [DOI: 10.3390/f11111190] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The breeding of forest trees is only a few decades old, and is a much more complicated, longer, and expensive endeavor than the breeding of agricultural crops. One breeding cycle for forest trees can take 20–30 years. Recent advances in genomics and molecular biology have revolutionized traditional plant breeding based on visual phenotype assessment: the development of different types of molecular markers has made genotype selection possible. Marker-assisted breeding can significantly accelerate the breeding process, but this method has not been shown to be effective for selection of complex traits on forest trees. This new method of genomic selection is based on the analysis of all effects of quantitative trait loci (QTLs) using a large number of molecular markers distributed throughout the genome, which makes it possible to assess the genomic estimated breeding value (GEBV) of an individual. This approach is expected to be much more efficient for forest tree improvement than traditional breeding. Here, we review the current state of the art in the application of genomic selection in forest tree breeding and discuss different methods of genotyping and phenotyping. We also compare the accuracies of genomic prediction models and highlight the importance of a prior cost-benefit analysis before implementing genomic selection. Perspectives for the further development of this approach in forest breeding are also discussed: expanding the range of species and the list of valuable traits, the application of high-throughput phenotyping methods, and the possibility of using epigenetic variance to improve of forest trees.
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