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Ismawanto S, Aji M, Lopez D, Mournet P, Gohet E, Syafaah A, Bonal F, Oktavia F, Taryono, Subandiyah S, Montoro P. Genetic analysis of agronomic and physiological traits associated with latex yield revealed complex genetic bases in Hevea brasiliensis. Heliyon 2024; 10:e33421. [PMID: 39040337 PMCID: PMC11260978 DOI: 10.1016/j.heliyon.2024.e33421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 06/17/2024] [Accepted: 06/21/2024] [Indexed: 07/24/2024] Open
Abstract
Hevea brasiliensis, a natural rubber producing species, is widely cultivated due to its high rubber yield potential. Natural rubber is synthesised in the rubber particles of laticifers. Latex diagnosis (LD) was established to characterise the physiological state of the laticiferous system by measuring its physiological parameters, i.e., sucrose, inorganic phosphorous (Pi), thiols and total solid content (TSC). Rubber clones are often classified in three groups i.e., quick starters, medium starters and slow starters. To better understand the genetic bases of latex yield, a biparental population was generated from a cross between the quick-starter clone PB 260 and the medium-starter clone SP 217. LD was performed during the peak latex production season and used to calculate sucrose loading. The agronomic and physiological parameters associated with latex yield led to the classification of genotypes according to the rubber clonal typology and to the identification of quantitative trait loci (QTL) using a high-density map. Inorganic phosphorous content was positively associated with yield during the first year of production thus enabling identification of quick-starter clones. In addition, the LD-based clonal typology led to determine the long-term yield potential and the use of appropriate ethephon stimulation. QTL analysis successfully identified several QTLs related to yield, sucrose, Pi and TSC. One QTL related to sucrose loading was identified in the same position as the QTL for sucrose on linkage group 1. To our knowledge, this is the first study to report QTL analysis for this trait. The use of a high-density map enables the identification of genes underlying QTLs. Several putative genes underlying QTLs related to yield, sucrose and TSC were identified.
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Affiliation(s)
- Sigit Ismawanto
- CIRAD, UMR AGAP Institut, F-34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398, Montpellier, France
- Faculty of Agriculture, Universitas Gadjah Mada, Bulaksumur, Sleman, Yogyakarta, 55281, Indonesia
- Pusat Penelitian Karet, Sembawa, Banyuasin, Sumatera Selatan, 30953, Indonesia
| | - Martini Aji
- Pusat Penelitian Karet, Sembawa, Banyuasin, Sumatera Selatan, 30953, Indonesia
| | - David Lopez
- CIRAD, UMR AGAP Institut, F-34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398, Montpellier, France
| | - Pierre Mournet
- CIRAD, UMR AGAP Institut, F-34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398, Montpellier, France
| | - Eric Gohet
- CIRAD, UMR ABsys, F-34398, Montpellier, France
| | - Afdholiatus Syafaah
- Pusat Penelitian Karet, Sembawa, Banyuasin, Sumatera Selatan, 30953, Indonesia
| | - Florelle Bonal
- CIRAD, UMR AGAP Institut, F-34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398, Montpellier, France
| | - Fetrina Oktavia
- Pusat Penelitian Karet, Sembawa, Banyuasin, Sumatera Selatan, 30953, Indonesia
| | - Taryono
- Faculty of Agriculture, Universitas Gadjah Mada, Bulaksumur, Sleman, Yogyakarta, 55281, Indonesia
| | - Siti Subandiyah
- Faculty of Agriculture, Universitas Gadjah Mada, Bulaksumur, Sleman, Yogyakarta, 55281, Indonesia
| | - Pascal Montoro
- CIRAD, UMR AGAP Institut, F-34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398, Montpellier, France
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Potapova NA, Zlobin AS, Leonova IN, Salina EA, Tsepilov YA. The BLUP method in evaluation of breeding values of Russian spring wheat lines using micro- and macroelements in seeds. Vavilovskii Zhurnal Genet Selektsii 2024; 28:456-462. [PMID: 39027122 PMCID: PMC11253017 DOI: 10.18699/vjgb-24-51] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/06/2024] [Accepted: 03/12/2023] [Indexed: 07/20/2024] Open
Abstract
Genomic selection is a technology that allows for the determination of the genetic value of varieties of agricultural plants and animal breeds, based on information about genotypes and phenotypes. The measured breeding value (BV) for varieties and breeds in relation to the target trait allows breeding stages to be thoroughly planned and the parent forms suitable for crossing to be chosen. In this work, the BLUP method was used to assess the breeding value of 149 Russian varieties and introgression lines (4 measurements for each variety or line, 596 phenotypic points) of spring wheat according to the content of seven chemical elements in the grain - K, Ca, Mg, Mn, Fe, Zn, Cu. The quality of the evaluation of breeding values was assessed using cross-validation, when the sample was randomly divided into five parts, one of which was chosen as a test population. The following average values of the Pearson correlation were obtained for predicting the concentration of trace elements: K - 0.67, Ca - 0.61, Mg - 0.4, Mn - 0.5, Fe - 0.38, Zn - 0.46, Cu - 0.48. Out of the 35 models studied, the p-value was below the nominal significant threshold (p-value < 0.05) for 28 models. For 11 models, the p-value was significant after correction for multiple testing (p-value < 0.001). For Ca and K, four out of five models and for Mn two out of five models had a p-value below the threshold adjusted for multiple testing. For 30 varieties that showed the best varietal values for Ca, K and Mn, the average breeding value was 296.43, 785.11 and 4.87 mg/kg higher, respectively, than the average breeding value of the population. The results obtained show the relevance of the application of genomic selection models even in such limited-size samples. The models for K, Ca and Mn are suitable for assessing the breeding value of Russian wheat varieties based on these characteristics.
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Affiliation(s)
- N A Potapova
- Kurchatov Genomic Center of ICG SB RAS, Novosibirsk, Russia Institute for Information Transmission Problems of the Russian Academy of Sciences (Kharkevich Institute), Moscow, Russia Lopukhin Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical-Biological Agency, Moscow, Russia
| | - A S Zlobin
- Kurchatov Genomic Center of ICG SB RAS, Novosibirsk, Russia
| | - I N Leonova
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - E A Salina
- Kurchatov Genomic Center of ICG SB RAS, Novosibirsk, Russia
| | - Y A Tsepilov
- Kurchatov Genomic Center of ICG SB RAS, Novosibirsk, Russia
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Weber SE, Roscher-Ehrig L, Kox T, Abbadi A, Stahl A, Snowdon RJ. Genomic prediction in Brassica napus: evaluating the benefit of imputed whole-genome sequencing data. Genome 2024; 67:210-222. [PMID: 38708850 DOI: 10.1139/gen-2023-0126] [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: 05/07/2024]
Abstract
Advances in sequencing technology allow whole plant genomes to be sequenced with high quality. Combining genotypic and phenotypic data in genomic prediction helps breeders to select crossing partners in partially phenotyped populations. In plant breeding programs, the cost of sequencing entire breeding populations still exceeds available genotyping budgets. Hence, the method for genotyping is still mainly single nucleotide polymorphism (SNP) arrays; however, arrays are unable to assess the entire genome- and population-wide diversity. A compromise involves genotyping the entire population using an SNP array and a subset of the population with whole-genome sequencing. Both datasets can then be used to impute markers from whole-genome sequencing onto the entire population. Here, we evaluate whether imputation of whole-genome sequencing data enhances genomic predictions, using data from a nested association mapping population of rapeseed (Brassica napus). Employing two cross-validation schemes that mimic scenarios for the prediction of close and distant relatives, we show that imputed marker data do not significantly improve prediction accuracy, likely due to redundancy in relationship estimates and imputation errors. In simulation studies, only small improvements were observed, further corroborating the findings. We conclude that SNP arrays are already equipped with the information that is added by imputation through relationship and linkage disequilibrium.
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Affiliation(s)
- Sven E Weber
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | - Lennard Roscher-Ehrig
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
| | | | | | - Andreas Stahl
- Julius Kuehn Institute (JKI), Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance, Quedlinburg, Germany
| | - Rod J Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Giessen, Germany
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Garzón-Martínez GA, Osorio-Guarín JA, Moreno LP, Bastidas S, Barrero LS, Lopez-Cruz M, Enciso-Rodríguez FE. Genomic selection for morphological and yield-related traits using genome-wide SNPs in oil palm. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:71. [PMID: 37313322 PMCID: PMC10248711 DOI: 10.1007/s11032-022-01341-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 10/29/2022] [Indexed: 06/15/2023]
Abstract
Oil palm is the most important oil crop worldwide. Colombia is the fourth largest producer, primarily relying on production from interspecific hybrids, derived from crosses between Elaeis oleifera and Elaeis guineensis (OxG). However, conventional breeding can take up to 20 years to generate a new variety. Therefore, reducing the breeding cycle while improving the genetic gain for complex traits is desirable. Genomic selection (GS) is an approach with the potential to achieve this goal. In this study, we evaluated 431 F1 interspecific hybrids (OxG) and 444 backcrosses (BC1) for morphological and yield-related traits. Genomic predictions were performed with the G-BLUP model using three different population datasets for training the model: the same population (TRN1), the other population (TRN2), and both populations (TRN1+2). Higher multi-family prediction accuracies were obtained for foliar area (0.3 in OxG) and trunk height (0.47 in BC1) when the model was trained with TRN1. Single-family prediction accuracies were lower in the OxG compared to BC1 families for traits such as trunk diameter, trunk height, bunch number, and yield using TRN1. Conversely, lower prediction accuracies were obtained for most traits when the model was trained using TRN2 (< 0.1). Multi-trait models showed a substantial increase of the predictions for traits such as yield (0.22 for OxG and 0.44 for BC1), because of the genetic correlations between traits. The results herein highlighted the potential of GS for parental selection in OxG and BC1 populations, but further studies are required to improve the models to select individuals by their genetic value. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01341-5.
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Affiliation(s)
- Gina A. Garzón-Martínez
- Centro de Investigación Tibaitatá, Corporación Colombiana de Investigación Agropecuaria (Agrosavia), Mosquera, Cundinamarca Colombia
| | - Jaime A. Osorio-Guarín
- Centro de Investigación Tibaitatá, Corporación Colombiana de Investigación Agropecuaria (Agrosavia), Mosquera, Cundinamarca Colombia
| | - Leidy P. Moreno
- Centro de Investigación Palmira, Corporación Colombiana de Investigación Agropecuaria (Agrosavia), Palmira, Valle del Cauca Colombia
| | - Silvio Bastidas
- Centro de Investigación Palmira, Corporación Colombiana de Investigación Agropecuaria (Agrosavia), Palmira, Valle del Cauca Colombia
| | - Luz Stella Barrero
- Centro de Investigación Tibaitatá, Corporación Colombiana de Investigación Agropecuaria (Agrosavia), Mosquera, Cundinamarca Colombia
| | - Marco Lopez-Cruz
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI USA
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI USA
| | - Felix E. Enciso-Rodríguez
- Centro de Investigación Tibaitatá, Corporación Colombiana de Investigación Agropecuaria (Agrosavia), Mosquera, Cundinamarca Colombia
- Blueberry Breeding Program, Department of Horticulture Sciences, University of Florida, 2211 Fifield Hall, 2550 Hull Rd, Gainesville, FL 32611 USA
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Mbo Nkoulou LF, Ngalle HB, Cros D, Adje COA, Fassinou NVH, Bell J, Achigan-Dako EG. Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species. FRONTIERS IN PLANT SCIENCE 2022; 13:953133. [PMID: 36388523 PMCID: PMC9650417 DOI: 10.3389/fpls.2022.953133] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 09/28/2022] [Indexed: 06/16/2023]
Abstract
Genomic selection (GS) in plant breeding is explored as a promising tool to solve the problems related to the biotic and abiotic threats. Polyploid plants like bananas (Musa spp.) face the problem of drought and black sigatoka disease (BSD) that restrict their production. The conventional plant breeding is experiencing difficulties, particularly phenotyping costs and long generation interval. To overcome these difficulties, GS in plant breeding is explored as an alternative with a great potential for reducing costs and time in selection process. So far, GS does not have the same success in polyploid plants as with diploid plants because of the complexity of their genome. In this review, we present the main constraints to the application of GS in polyploid plants and the prospects for overcoming these constraints. Particular emphasis is placed on breeding for BSD and drought-two major threats to banana production-used in this review as a model of polyploid plant. It emerges that the difficulty in obtaining markers of good quality in polyploids is the first challenge of GS on polyploid plants, because the main tools used were developed for diploid species. In addition to that, there is a big challenge of mastering genetic interactions such as dominance and epistasis effects as well as the genotype by environment interaction, which are very common in polyploid plants. To get around these challenges, we have presented bioinformatics tools, as well as artificial intelligence approaches, including machine learning. Furthermore, a scheme for applying GS to banana for BSD and drought has been proposed. This review is of paramount impact for breeding programs that seek to reduce the selection cycle of polyploids despite the complexity of their genome.
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Affiliation(s)
- Luther Fort Mbo Nkoulou
- Genetics, Biotechnology, and Seed Science Unit (GBioS), Department of Plant Sciences, Faculty of Agronomic Sciences, University of Abomey Calavi, Cotonou, Benin
- Unit of Genetics and Plant Breeding (UGAP), Department of Plant Biology, Faculty of Sciences, University of Yaoundé 1, Yaoundé, Cameroon
- Institute of Agricultural Research for Development, Centre de Recherche Agricole de Mbalmayo (CRAM), Mbalmayo, Cameroon
| | - Hermine Bille Ngalle
- Unit of Genetics and Plant Breeding (UGAP), Department of Plant Biology, Faculty of Sciences, University of Yaoundé 1, Yaoundé, Cameroon
| | - David Cros
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche (UMR) Amélioration Génétique et Adaptation des Plantes méditerranéennes et tropicales (AGAP) Institut, Montpellier, France
- Unité Mixte de Recherche (UMR) Amélioration Génétique et Adaptation des Plantes méditerranéennes et tropicales (AGAP) Institut, University of Montpellier, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut Agro, Montpellier, France
| | - Charlotte O. A. Adje
- Genetics, Biotechnology, and Seed Science Unit (GBioS), Department of Plant Sciences, Faculty of Agronomic Sciences, University of Abomey Calavi, Cotonou, Benin
| | - Nicodeme V. H. Fassinou
- Genetics, Biotechnology, and Seed Science Unit (GBioS), Department of Plant Sciences, Faculty of Agronomic Sciences, University of Abomey Calavi, Cotonou, Benin
| | - Joseph Bell
- Unit of Genetics and Plant Breeding (UGAP), Department of Plant Biology, Faculty of Sciences, University of Yaoundé 1, Yaoundé, Cameroon
| | - Enoch G. Achigan-Dako
- Genetics, Biotechnology, and Seed Science Unit (GBioS), Department of Plant Sciences, Faculty of Agronomic Sciences, University of Abomey Calavi, Cotonou, Benin
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Zhang X, Zhao Y, Kou Y, Chen X, Yang J, Zhang H, Zhao Z, Zhao Y, Zhao G, Li Z. Diploid chromosome-level reference genome and population genomic analyses provide insights into Gypenoside biosynthesis and demographic evolution of Gynostemma pentaphyllum (Cucurbitaceae). HORTICULTURE RESEARCH 2022; 10:uhac231. [PMID: 36643751 PMCID: PMC9832869 DOI: 10.1093/hr/uhac231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 10/01/2022] [Indexed: 06/17/2023]
Abstract
Gynostemma pentaphyllum (Thunb.) Makino is a perennial creeping herbaceous plant in the family Cucurbitaceae, which has great medicinal value and commercial potential, but urgent conservation efforts are needed due to the gradual decreases and fragmented distribution of its wild populations. Here, we report the high-quality diploid chromosome-level genome of G. pentaphyllum obtained using a combination of next-generation sequencing short reads, Nanopore long reads, and Hi-C sequencing technologies. The genome is anchored to 11 pseudo-chromosomes with a total size of 608.95 Mb and 26 588 predicted genes. Comparative genomic analyses indicate that G. pentaphyllum is estimated to have diverged from Momordica charantia 60.7 million years ago, with no recent whole-genome duplication event. Genomic population analyses based on genotyping-by-sequencing and ecological niche analyses indicated low genetic diversity but a strong population structure within the species, which could classify 32 G. pentaphyllum populations into three geographical groups shaped jointly by geographic and climate factors. Furthermore, comparative transcriptome analyses showed that the genes encoding enzyme involved in gypenoside biosynthesis had higher expression levels in the leaves and tendrils. Overall, the findings obtained in this study provide an effective molecular basis for further studies of demographic genetics, ecological adaption, and systematic evolution in Cucurbitaceae species, as well as contributing to molecular breeding, and the biosynthesis and biotransformation of gypenoside.
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Affiliation(s)
- Xiao Zhang
- Key Laboratory of Resource Biology and Biotechnology in Western China (Ministry of Education), College of Life Sciences, Northwest University, Xi’an, Shaanxi, 710069, China
| | - Yuhe Zhao
- Key Laboratory of Resource Biology and Biotechnology in Western China (Ministry of Education), College of Life Sciences, Northwest University, Xi’an, Shaanxi, 710069, China
| | - Yixuan Kou
- Laboratory of Subtropical Biodiversity, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Xiaodan Chen
- Key Laboratory of Resource Biology and Biotechnology in Western China (Ministry of Education), College of Life Sciences, Northwest University, Xi’an, Shaanxi, 710069, China
- College of Life Sciences, Shanxi Normal University, Taiyuan, Shanxi, 030012, China
| | - Jia Yang
- Key Laboratory of Resource Biology and Biotechnology in Western China (Ministry of Education), College of Life Sciences, Northwest University, Xi’an, Shaanxi, 710069, China
| | - Hao Zhang
- Key Laboratory of Resource Biology and Biotechnology in Western China (Ministry of Education), College of Life Sciences, Northwest University, Xi’an, Shaanxi, 710069, China
- College of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, 510275, China
| | - Zhe Zhao
- Key Laboratory of Resource Biology and Biotechnology in Western China (Ministry of Education), College of Life Sciences, Northwest University, Xi’an, Shaanxi, 710069, China
| | - Yuemei Zhao
- School of Biological Sciences, Guizhou Education University, Guiyang, Guizhou, 550018, China
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Seyum EG, Bille NH, Abtew WG, Munyengwa N, Bell JM, Cros D. Genomic selection in tropical perennial crops and plantation trees: a review. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:58. [PMID: 37313015 PMCID: PMC10248687 DOI: 10.1007/s11032-022-01326-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 09/06/2022] [Indexed: 06/15/2023]
Abstract
To overcome the multiple challenges currently faced by agriculture, such as climate change and soil deterioration, more efficient plant breeding strategies are required. Genomic selection (GS) is crucial for the genetic improvement of quantitative traits, as it can increase selection intensity, shorten the generation interval, and improve selection accuracy for traits that are difficult to phenotype. Tropical perennial crops and plantation trees are of major economic importance and have consequently been the subject of many GS articles. In this review, we discuss the factors that affect GS accuracy (statistical models, linkage disequilibrium, information concerning markers, relatedness between training and target populations, the size of the training population, and trait heritability) and the genetic gain expected in these species. The impact of GS will be particularly strong in tropical perennial crops and plantation trees as they have long breeding cycles and constrained selection intensity. Future GS prospects are also discussed. High-throughput phenotyping will allow constructing of large training populations and implementing of phenomic selection. Optimized modeling is needed for longitudinal traits and multi-environment trials. The use of multi-omics, haploblocks, and structural variants will enable going beyond single-locus genotype data. Innovative statistical approaches, like artificial neural networks, are expected to efficiently handle the increasing amounts of heterogeneous multi-scale data. Targeted recombinations on sites identified from profiles of marker effects have the potential to further increase genetic gain. GS can also aid re-domestication and introgression breeding. Finally, GS consortia will play an important role in making the best of these opportunities. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01326-4.
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Affiliation(s)
- Essubalew Getachew Seyum
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
- Department of Horticulture and Plant Sciences, College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box 307, Jimma, Ethiopia
| | - Ngalle Hermine Bille
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
| | - Wosene Gebreselassie Abtew
- Department of Horticulture and Plant Sciences, College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box 307, Jimma, Ethiopia
| | - Norman Munyengwa
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Joseph Martin Bell
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
| | - David Cros
- CIRAD, UMR AGAP Institut, 34398 Montpellier, France
- UMR AGAP Institut, CIRAD, INRAE, Univ. Montpellier, Institut Agro, 34398 Montpellier, France
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Rajendran NR, Qureshi N, Pourkheirandish M. Genotyping by Sequencing Advancements in Barley. FRONTIERS IN PLANT SCIENCE 2022; 13:931423. [PMID: 36003814 PMCID: PMC9394214 DOI: 10.3389/fpls.2022.931423] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 06/20/2022] [Indexed: 06/15/2023]
Abstract
Barley is considered an ideal crop to study cereal genetics due to its close relationship with wheat and diploid ancestral genome. It plays a crucial role in reducing risks to global food security posed by climate change. Genetic variations in the traits of interest in crops are vital for their improvement. DNA markers have been widely used to estimate these variations in populations. With the advancements in next-generation sequencing, breeders could access different types of genetic variations within different lines, with single-nucleotide polymorphisms (SNPs) being the most common type. However, genotyping barley with whole genome sequencing (WGS) is challenged by the higher cost and computational demand caused by the large genome size (5.5GB) and a high proportion of repetitive sequences (80%). Genotyping-by-sequencing (GBS) protocols based on restriction enzymes and target enrichment allow a cost-effective SNP discovery by reducing the genome complexity. In general, GBS has opened up new horizons for plant breeding and genetics. Though considered a reliable alternative to WGS, GBS also presents various computational difficulties, but GBS-specific pipelines are designed to overcome these challenges. Moreover, a robust design for GBS can facilitate the imputation to the WGS level of crops with high linkage disequilibrium. The complete exploitation of GBS advancements will pave the way to a better understanding of crop genetics and offer opportunities for the successful improvement of barley and its close relatives.
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Affiliation(s)
- Nirmal Raj Rajendran
- Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC, Australia
| | - Naeela Qureshi
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco, Estado de Mexico, Mexico
| | - Mohammad Pourkheirandish
- Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC, Australia
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