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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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Ousmael KM, Cappa EP, Hansen JK, Hendre P, Hansen OK. Genomic evaluation for breeding and genetic management in Cordia africana, a multipurpose tropical tree species. BMC Genomics 2024; 25:9. [PMID: 38166623 PMCID: PMC10759591 DOI: 10.1186/s12864-023-09907-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 12/14/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Planting tested forest reproductive material is crucial to ensure the increased resilience of intensively managed productive stands for timber and wood product markets under climate change scenarios. Single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) analysis is a cost-effective option for using genomic tools to enhance the accuracy of predicted breeding values and genetic parameter estimation in forest tree species. Here, we tested the efficiency of ssGBLUP in a tropical multipurpose tree species, Cordia africana, by partial population genotyping. A total of 8070 trees from three breeding seedling orchards (BSOs) were phenotyped for height. We genotyped 6.1% of the phenotyped individuals with 4373 single nucleotide polymorphisms. The results of ssGBLUP were compared with pedigree-based best linear unbiased prediction (ABLUP) and genomic best linear unbiased prediction (GBLUP), based on genetic parameters, theoretical accuracy of breeding values, selection candidate ranking, genetic gain, and predictive accuracy and prediction bias. RESULTS Genotyping a subset of the study population provided insights into the level of relatedness in BSOs, allowing better genetic management. Due to the inbreeding detected within the genotyped provenances, we estimated genetic parameters both with and without accounting for inbreeding. The ssGBLUP model showed improved performance in terms of additive genetic variance and theoretical breeding value accuracy. Similarly, ssGBLUP showed improved predictive accuracy and lower bias than the pedigree-based relationship matrix (ABLUP). CONCLUSIONS This study of C. africana, a species in decline due to deforestation and selective logging, revealed inbreeding depression. The provenance exhibiting the highest level of inbreeding had the poorest overall performance. The use of different relationship matrices and accounting for inbreeding did not substantially affect the ranking of candidate individuals. This is the first study of this approach in a tropical multipurpose tree species, and the analysed BSOs represent the primary effort to breed C. africana.
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Affiliation(s)
- Kedra M Ousmael
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23, 1958, Frederiksberg C, Denmark.
| | - Eduardo P Cappa
- Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Recursos Biológicos, Centro de Investigación en Recursos Naturales, De Los Reseros y Dr. Nicolás Repetto s/n, 1686, Hurlingham, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Jon K Hansen
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23, 1958, Frederiksberg C, Denmark
| | - Prasad Hendre
- World Agroforestry Centre (ICRAF), United Nations Avenue, Nairobi, 00100, Kenya
| | - Ole K Hansen
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23, 1958, Frederiksberg C, Denmark
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Malmberg MM, Smith C, Thakur P, Drayton MC, Wilson J, Shinozuka M, Clayton W, Inch C, Spangenberg GC, Smith KF, Cogan NOI, Pembleton LW. Developing an integrated genomic selection approach beyond biomass for varietal protection and nutritive traits in perennial ryegrass (Lolium perenne L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:44. [PMID: 36897387 PMCID: PMC10006259 DOI: 10.1007/s00122-023-04263-8] [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: 07/01/2022] [Accepted: 10/21/2022] [Indexed: 06/18/2023]
Abstract
Breeding target traits can be broadened to include nutritive value and plant breeder's rights traits in perennial ryegrass by using in-field regression-based spectroscopy phenotyping and genomic selection. Perennial ryegrass breeding has focused on biomass yield, but expansion into a broader set of traits is needed to benefit livestock industries whilst also providing support for intellectual property protection of cultivars. Numerous breeding objectives can be targeted simultaneously with the development of sensor-based phenomics and genomic selection (GS). Of particular interest are nutritive value (NV), which has been difficult and expensive to measure using traditional phenotyping methods, resulting in limited genetic improvement to date, and traits required to obtain varietal protection, known as plant breeder's rights (PBR) traits. In order to assess phenotyping requirements for NV improvement and potential for genetic improvement, in-field reflectance-based spectroscopy was assessed and GS evaluated in a single population for three key NV traits, captured across four timepoints. Using three prediction approaches, the possibility of targeting PBR traits using GS was evaluated for five traits recorded across three years of a breeding program. Prediction accuracy was generally low to moderate for NV traits and moderate to high for PBR traits, with heritability highly correlated with GS accuracy. NV did not show significant or consistent correlation between timepoints highlighting the need to incorporate seasonal NV into selection indexes and the value of being able to regularly monitor NV across seasons. This study has demonstrated the ability to implement GS for both NV and PBR traits in perennial ryegrass, facilitating the expansion of ryegrass breeding targets to agronomically relevant traits while ensuring necessary varietal protection is achieved.
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Affiliation(s)
- M M Malmberg
- AgriBio, Centre for AgriBioscience, Agriculture Victoria Research, Bundoora, VIC, 3083, Australia.
| | - C Smith
- Hamilton Centre, Agriculture Victoria Research, Hamilton, VIC, 3300, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3086, Australia
| | - P Thakur
- AgriBio, Centre for AgriBioscience, Agriculture Victoria Research, Bundoora, VIC, 3083, Australia
| | - M C Drayton
- AgriBio, Centre for AgriBioscience, Agriculture Victoria Research, Bundoora, VIC, 3083, Australia
| | - J Wilson
- AgriBio, Centre for AgriBioscience, Agriculture Victoria Research, Bundoora, VIC, 3083, Australia
| | - M Shinozuka
- AgriBio, Centre for AgriBioscience, Agriculture Victoria Research, Bundoora, VIC, 3083, Australia
| | - W Clayton
- Barenbrug New Zealand, 2547 Old West Coast Road, Christchurch, 7671, New Zealand
| | - C Inch
- Barenbrug New Zealand, 2547 Old West Coast Road, Christchurch, 7671, New Zealand
| | - G C Spangenberg
- AgriBio, Centre for AgriBioscience, Agriculture Victoria Research, Bundoora, VIC, 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3086, Australia
| | - K F Smith
- Hamilton Centre, Agriculture Victoria Research, Hamilton, VIC, 3300, Australia
- Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - N O I Cogan
- AgriBio, Centre for AgriBioscience, Agriculture Victoria Research, Bundoora, VIC, 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3086, Australia
| | - L W Pembleton
- AgriBio, Centre for AgriBioscience, Agriculture Victoria Research, Bundoora, VIC, 3083, Australia
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Civantos-Gómez I, Rubio Teso ML, Galeano J, Rubiales D, Iriondo JM, García-Algarra J. Climate change conditions the selection of rust-resistant candidate wild lentil populations for in situ conservation. FRONTIERS IN PLANT SCIENCE 2022; 13:1010799. [PMID: 36407589 PMCID: PMC9669080 DOI: 10.3389/fpls.2022.1010799] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 10/14/2022] [Indexed: 06/02/2023]
Abstract
Crop Wild Relatives (CWR) are a valuable source of genetic diversity that can be transferred to commercial crops, so their conservation will become a priority in the face of climate change. Bizarrely, in situ conserved CWR populations and the traits one might wish to preserve in them are themselves vulnerable to climate change. In this study, we used a quantitative machine learning predictive approach to project the resistance of CWR populations of lentils to a common disease, lentil rust, caused by fungus Uromyces viciae-fabae. Resistance is measured through a proxy quantitative value, DSr (Disease Severity relative), quite complex and expensive to get. Therefore, machine learning is a convenient tool to predict this magnitude using a well-curated georeferenced calibration set. Previous works have provided a binary outcome (resistant vs. non-resistant), but that approach is not fine enough to answer three practical questions: which variables are key to predict rust resistance, which CWR populations are resistant to rust under current environmental conditions, and which of them are likely to keep this trait under different climate change scenarios. We first predict rust resistance in present time for crop wild relatives that grow up inside protected areas. Then, we use the same models under future climate IPCC (Intergovernmental Panel on Climate Change) scenarios to predict future DSr values. Populations that are rust-resistant by now and under future conditions are optimal candidates for further evaluation and in situ conservation of this valuable trait. We have found that rust-resistance variation as a result of climate change is not uniform across the geographic scope of the study (the Mediterranean basin), and that candidate populations share some interesting common environmental conditions.
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Affiliation(s)
- Iciar Civantos-Gómez
- Complex System Group, Universidad Politécnica de Madrid, Madrid, Spain
- Faculty of Economics and Business Administration, Universidad Pontificia Comillas, Madrid, Spain
| | - María Luisa Rubio Teso
- ECOEVO Research Group, Área de Biodiversidad y Conservación, Universidad Rey Juan Carlos, Madrid, Spain
| | - Javier Galeano
- Complex System Group, Universidad Politécnica de Madrid, Madrid, Spain
| | - Diego Rubiales
- Instituto de Agricultura Sostenible (CSIC) Avenida Menéndez Pidal s/n Campus Alameda del Obispo, Córdoba, Spain
| | - José María Iriondo
- ECOEVO Research Group, Área de Biodiversidad y Conservación, Universidad Rey Juan Carlos, Madrid, Spain
| | - Javier García-Algarra
- DRACO Research Group, Centro Universitario de Tecnología y Arte Digital, Las Rozas, Spain
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Barre P, Asp T, Byrne S, Casler M, Faville M, Rognli OA, Roldan-Ruiz I, Skøt L, Ghesquière M. Genomic Prediction of Complex Traits in Forage Plants Species: Perennial Grasses Case. Methods Mol Biol 2022; 2467:521-541. [PMID: 35451789 DOI: 10.1007/978-1-0716-2205-6_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The majority of forage grass species are obligate outbreeders. Their breeding classically consists of an initial selection on spaced plants for highly heritable traits such as disease resistances and heading date, followed by familial selection on swards for forage yield and quality traits. The high level of diversity and heterozygosity, and associated decay of linkage disequilibrium (LD) over very short genomic distances, has hampered the implementation of genomic selection (GS) in these species. However, next generation sequencing technologies in combination with the development of genomic resources have recently facilitated implementation of GS in forage grass species such as perennial ryegrass (Lolium perenne L.), switchgrass (Panicum virgatum L.), and timothy (Phleum pratense L.). Experimental work and simulations have shown that GS can increase significantly the genetic gain per unit of time for traits with different levels of heritability. The main reasons are (1) the possibility to select single plants based on their genomic estimated breeding values (GEBV) for traits measured at sward level, (2) a reduction in the duration of selection cycles, and less importantly (3) an increase in the selection intensity associated with an increase in the genetic variance used for selection. Nevertheless, several factors should be taken into account for the successful implementation of GS in forage grasses. For example, it has been shown that the level of relatedness between the training and the selection population is particularly critical when working with highly structured meta-populations consisting of several genetic groups. A sufficient number of markers should be used to estimate properly the kinship between individuals and to reflect the variability of major QTLs. It is also important that the prediction models are trained for relevant environments when dealing with traits with high genotype × environment interaction (G × E). Finally, in these outbreeding species, measures to reduce inbreeding should be used to counterbalance the high selection intensity that can be achieved in GS.
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Affiliation(s)
| | - Torben Asp
- Center for Quantitative Genetics and Genomics, Aarhus University, Slagelse, Denmark
| | - Stephen Byrne
- Teagasc, Crop Science Department, Oak Park, Carlow, Ireland
| | - Michael Casler
- U.S. Dairy Forage Research Center, USDA-ARS, Madison, WI, USA
| | - Marty Faville
- AgResearch Ltd , Grasslands Research Centre, Palmerston North, New Zealand
| | - Odd Arne Rognli
- Department of Plant Sciences, Faculty of Biosciences, Norwegian, University of Life Sciences (NMBU), Ås, Norway
| | - Isabel Roldan-Ruiz
- Flanders Research Institute for Agriculture, Fisheries and Food (ILVO)-Plant Sciences Unit, Melle, Belgium
| | - Leif Skøt
- IBERS, Aberystwyth University, Ceredigion, UK
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Wang Q, Yan T, Long Z, Huang LY, Zhu Y, Xu Y, Chen X, Pak H, Li J, Wu D, Xu Y, Hua S, Jiang L. Prediction of heterosis in the recent rapeseed (Brassica napus) polyploid by pairing parental nucleotide sequences. PLoS Genet 2021; 17:e1009879. [PMID: 34735437 PMCID: PMC8608326 DOI: 10.1371/journal.pgen.1009879] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 11/22/2021] [Accepted: 10/15/2021] [Indexed: 11/19/2022] Open
Abstract
The utilization of heterosis is a successful strategy in increasing yield for many crops. However, it consumes tremendous manpower to test the combining ability of the parents in fields. Here, we applied the genomic-selection (GS) strategy and developed models that significantly increase the predictability of heterosis by introducing the concept of a regional parental genetic-similarity index (PGSI) and reducing dimension in the calculation matrix in a machine-learning approach. Overall, PGSI negatively affected grain yield and several other traits but positively influenced the thousand-seed weight of the hybrids. It was found that the C subgenome of rapeseed had a greater impact on heterosis than the A subgenome. We drew maps with overviews of quantitative-trait loci that were responsible for the heterosis (h-QTLs) of various agronomic traits. Identifications and annotations of genes underlying high impacting h-QTLs were provided. Using models that we elaborated, combining abilities between an Ogu-CMS-pool member and a potential restorer can be simulated in silico, sidestepping laborious work, such as testing crosses in fields. The achievements here provide a case of heterosis prediction in polyploid genomes with relatively large genome sizes. Oilseed rape (Brassica napus) is of significant economic interest worldwide, providing high-quality oil with excellent health-promoting properties. It represents an excellent model of a successful recent polyploid that rapidly became an important crop worldwide. The utilization of hybridization, leading to hybrid vigor, or heterosis, is a successful strategy in increasing yield and vigor for many field crops including rapeseed (Brassica napus). However, the procedure of using classical breeding methods remains slow and laborious, illustrating the need for predictive and innovative methods. Here, we have achieved a significant breakthrough by using genome selection and significantly advanced models to predict the heterosis by pairing genome-wide nucleotides of parents. We provided maps with overviews of quantitative trait loci that were responsible for the heterosis of various agronomic traits. The research used deep resequencing (>30x) data of the entire polyploidy rapeseed genome, providing a successful case for the prediction of heterosis in polyploid genomes with relatively large genome sizes. Moreover, we provided the genetic information (SNPs) of 1007 core accessions of this species in the public domain for testing combinations with high heterosis using our predicting model for rapeseed breeders all over the world.
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Affiliation(s)
- Qian Wang
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Tao Yan
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Zhengbiao Long
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Luna Yue Huang
- Department of Agricultural and Resource Economics, University of California, Berkeley, California, United States of America
| | - Yang Zhu
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Ying Xu
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Xiaoyang Chen
- Institute of Crop Science, Jinhua Academy of Agricultural Sciences, Jinhua, China
| | - Haksong Pak
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Jiqiang Li
- Institute of Crop Science, Zhangye Academy of Agricultural Sciences, Zhangye, China
| | - Dezhi Wu
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Yang Xu
- Agricultural College, Yangzhou University, Yangzhou, China
- * E-mail: (YX); (SH); (LJ)
| | - Shuijin Hua
- Institute of Crop and Nuclear Agricultural Sciences, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
- * E-mail: (YX); (SH); (LJ)
| | - Lixi Jiang
- Institute of Crop Science, Zhejiang University, Hangzhou, China
- * E-mail: (YX); (SH); (LJ)
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Amas J, Anderson R, Edwards D, Cowling W, Batley J. Status and advances in mining for blackleg (Leptosphaeria maculans) quantitative resistance (QR) in oilseed rape (Brassica napus). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:3123-3145. [PMID: 34104999 PMCID: PMC8440254 DOI: 10.1007/s00122-021-03877-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 05/29/2021] [Indexed: 05/04/2023]
Abstract
KEY MESSAGE Quantitative resistance (QR) loci discovered through genetic and genomic analyses are abundant in the Brassica napus genome, providing an opportunity for their utilization in enhancing blackleg resistance. Quantitative resistance (QR) has long been utilized to manage blackleg in Brassica napus (canola, oilseed rape), even before major resistance genes (R-genes) were extensively explored in breeding programmes. In contrast to R-gene-mediated qualitative resistance, QR reduces blackleg symptoms rather than completely eliminating the disease. As a polygenic trait, QR is controlled by numerous genes with modest effects, which exerts less pressure on the pathogen to evolve; hence, its effectiveness is more durable compared to R-gene-mediated resistance. Furthermore, combining QR with major R-genes has been shown to enhance resistance against diseases in important crops, including oilseed rape. For these reasons, there has been a renewed interest among breeders in utilizing QR in crop improvement. However, the mechanisms governing QR are largely unknown, limiting its deployment. Advances in genomics are facilitating the dissection of the genetic and molecular underpinnings of QR, resulting in the discovery of several loci and genes that can be potentially deployed to enhance blackleg resistance. Here, we summarize the efforts undertaken to identify blackleg QR loci in oilseed rape using linkage and association analysis. We update the knowledge on the possible mechanisms governing QR and the advances in searching for the underlying genes. Lastly, we lay out strategies to accelerate the genetic improvement of blackleg QR in oilseed rape using improved phenotyping approaches and genomic prediction tools.
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Affiliation(s)
- Junrey Amas
- School of Biological Sciences and The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6001 Australia
| | - Robyn Anderson
- School of Biological Sciences and The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6001 Australia
| | - David Edwards
- School of Biological Sciences and The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6001 Australia
| | - Wallace Cowling
- School of Agriculture and Environment and The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009 Australia
| | - Jacqueline Batley
- School of Biological Sciences and The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6001 Australia
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A comparison of shared patterns of differential gene expression and gene ontologies in response to water-stress in roots and leaves of four diverse genotypes of Lolium and Festuca spp. temperate pasture grasses. PLoS One 2021; 16:e0249636. [PMID: 33831050 PMCID: PMC8031407 DOI: 10.1371/journal.pone.0249636] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 03/22/2021] [Indexed: 12/13/2022] Open
Abstract
Ryegrasses (Lolium spp.) and fescues (Festuca spp.) are closely related and widely cultivated perennial forage grasses. As such, resilience in the face of abiotic stresses is an important component of their traits. We have compared patterns of differentially expressed genes (DEGs) in roots and leaves of two perennial ryegrass genotypes and a single genotype of each of a festulolium (predominantly Italian ryegrass) and meadow fescue with the onset of water stress, focussing on overall patterns of DEGs and gene ontology terms (GOs) shared by all four genotypes. Plants were established in a growing medium of vermiculite watered with nutrient solution. Leaf and root material were sampled at 35% (saturation) and, as the medium dried, at 15%, 5% and 1% estimated water contents (EWCs) and RNA extracted. Differential gene expression was evaluated comparing the EWC sampling points from RNAseq data using a combination of analysis methods. For all genotypes, the greatest numbers of DEGs were identified in the 35/1 and 5/1 comparisons in both leaves and roots. In total, 566 leaf and 643 root DEGs were common to all 4 genotypes, though a third of these leaf DEGs were not regulated in the same up/down direction in all 4 genotypes. For roots, the equivalent figure was 1% of the DEGs. GO terms shared by all four genotypes were often enriched by both up- and down-regulated DEGs in the leaf, whereas generally, only by either up- or down-regulated DEGs in the root. Overall, up-regulated leaf DEGs tended to be more genotype-specific than down-regulated leaf DEGs or root DEGs and were also associated with fewer GOs. On average, only 5–15% of the DEGs enriching common GO terms were shared by all 4 genotypes, suggesting considerable variation in DEGs between related genotypes in enacting similar biological processes.
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Zhao X, Nie G, Yao Y, Ji Z, Gao J, Wang X, Jiang Y. Natural variation and genomic prediction of growth, physiological traits, and nitrogen-use efficiency in perennial ryegrass under low-nitrogen stress. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:6670-6683. [PMID: 32827031 DOI: 10.1093/jxb/eraa388] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 08/17/2020] [Indexed: 06/11/2023]
Abstract
Genomic prediction of nitrogen-use efficiency (NUE) has not previously been studied in perennial grass species exposed to low-N stress. Here, we conducted a genomic prediction of physiological traits and NUE in 184 global accessions of perennial ryegrass (Lolium perenne) in response to a normal (7.5 mM) and low (0.75 mM) supply of N. After 21 d of treatment under greenhouse conditions, significant variations in plant height increment (ΔHT), leaf fresh weight (LFW), leaf dry weight (LDW), chlorophyll index (Chl), chlorophyll fluorescence, leaf N and carbon (C) contents, C/N ratio, and NUE were observed in accessions , but to a greater extent under low-N stress. Six genomic prediction models were applied to the data, namely the Bayesian method Bayes C, Bayesian LASSO, Bayesian Ridge Regression, Ridge Regression-Best Linear Unbiased Prediction, Reproducing Kernel Hilbert Spaces, and randomForest. These models produced similar prediction accuracy of traits within the normal or low-N treatments, but the accuracy differed between the two treatments. ΔHT, LFW, LDW, and C were predicted slightly better under normal N with a mean Pearson r-value of 0.26, compared with r=0.22 under low N, while the prediction accuracies for Chl, N, C/N, and NUE were significantly improved under low-N stress with a mean r=0.45, compared with r=0.26 under normal N. The population panel contained three population structures, which generally had no effect on prediction accuracy. The moderate prediction accuracies obtained for N, C, and NUE under low-N stress are promising, and suggest a feasible means by which germplasm might be initially assessed for further detailed studies in breeding programs.
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Affiliation(s)
- Xiongwei Zhao
- College of Life Sciences, Shanxi Agricultural University, Taigu, Shanxi Province, China
| | - Gang Nie
- Department of Grassland Science, Animal Science and Technology College, Sichuan Agricultural University, Chengdu, Sichuan Province, China
| | - Yanyu Yao
- Department of Agronomy, Purdue University, West Lafayette, IN, USA
| | - Zhongjie Ji
- Department of Agronomy, Purdue University, West Lafayette, IN, USA
| | - Jianhua Gao
- College of Life Sciences, Shanxi Agricultural University, Taigu, Shanxi Province, China
| | - Xingchun Wang
- College of Life Sciences, Shanxi Agricultural University, Taigu, Shanxi Province, China
| | - Yiwei Jiang
- Department of Agronomy, Purdue University, West Lafayette, IN, USA
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Effects of Different Strategies for Exploiting Genomic Selection in Perennial Ryegrass Breeding Programs. G3-GENES GENOMES GENETICS 2020; 10:3783-3795. [PMID: 32819970 PMCID: PMC7534426 DOI: 10.1534/g3.120.401382] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Genomic selection (GS) is a potential pathway to accelerate genetic gain for perennial ryegrass (Lolium perenne L.). The main objectives of the present study were to investigate the level of genetic gain and accuracy by applying GS in commercial perennial ryegrass breeding programs. Different scenarios were compared to a conventional breeding program. Simulated scenarios differed in the method of selection and structure of the breeding program. Two scenarios (Phen-Y12 and Phen) for phenotypic selection and three scenarios (GS-Y12, GS and GS-SP) were considered for genomic breeding schemes. All breeding schemes were simulated for 25 cycles. The amount of genetic gain achieved was different across scenarios. Compared to phenotypic scenarios, GS scenarios resulted in substantially larger genetic gain for the simulated traits. This was mainly due to more efficient selection of plots and single plants based on genomic estimated breeding values. Also, GS allows for reduction in waiting time for the availability of the superior genetic materials from previous cycles, which led to at least a doubling or a trebling of genetic gain compared to the traditional program. Reduction in additive genetic variance levels were higher with GS scenarios than with phenotypic selection. The results demonstrated that implementation of GS in ryegrass breeding is possible and presents an opportunity to make very significant improvements in genetic gains.
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11
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McNish IG, Zimmer CM, Susko AQ, Heuschele DJ, Tiede T, Case AJ, Smith KP. Mapping crown rust resistance at multiple time points in elite oat germplasm. THE PLANT GENOME 2020; 13:e20007. [PMID: 33016637 DOI: 10.1002/tpg2.20007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 10/22/2019] [Indexed: 06/11/2023]
Abstract
Crown rust, caused by Puccinia coronata f. sp. avenae Erikss., is the most important disease impacting cultivated oat (Avena sativa L.). Genetic resistance is the most desirable management strategy. The genetic architecture of crown rust resistance is not fully understood, and previous mapping investigations have mostly ignored temporal variation. A collection of elite oat lines sourced from oat breeding programs in the American Upper Midwest and Canada was genotyped using a high-density genotyping-by-sequencing system and evaluated for crown rust disease severity at multiple time points throughout the growing season in three disease nursery environments. Genome-wide association mapping was conducted for disease severity on each observation date of each trial, area under the disease progress curve for each trial, heading date for each trial, and area under the disease progress curve in a multi-environment model. Crown rust resistance quantitative trait loci (QTL) were detected on linkage groups Mrg05, Mrg12, Mrg15, Mrg18, Mrg20, and Mrg33. None of these QTL were coincident with a days-to-heading QTL detected on Mrg02. Only the QTL detected on Mrg15 was detected in multiple mapping models. The QTL on Mrg05, Mrg12, Mrg18, Mrg20, and Mrg33 were detected on only a single observation date and were not detected on observations just days before and after. This result uncovers the importance of temporal variation in mapping experiments which is usually ignored. It is possible that high density temporal data could be used to more precisely characterize the nature of plant resistance in other systems.
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Affiliation(s)
- Ian G McNish
- Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, 1991 Upper Buford Circle, 411 Borlaug Hall, St. Paul, MN, 55108
| | - Cristiano M Zimmer
- Dep. of Crop Science, Federal Univ. of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Alexander Q Susko
- Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, 1991 Upper Buford Circle, 411 Borlaug Hall, St. Paul, MN, 55108
| | - D Jo Heuschele
- Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, 1991 Upper Buford Circle, 411 Borlaug Hall, St. Paul, MN, 55108
| | | | | | - Kevin P Smith
- Dep. of Agronomy and Plant Genetics, Univ. of Minnesota, 1991 Upper Buford Circle, 411 Borlaug Hall, St. Paul, MN, 55108
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12
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Arojju SK, Cao M, Zulfi Jahufer MZ, Barrett BA, Faville MJ. Genomic Predictive Ability for Foliar Nutritive Traits in Perennial Ryegrass. G3 (BETHESDA, MD.) 2020; 10:695-708. [PMID: 31792009 PMCID: PMC7003077 DOI: 10.1534/g3.119.400880] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 11/25/2019] [Indexed: 11/24/2022]
Abstract
Forage nutritive value impacts animal nutrition, which underpins livestock productivity, reproduction and health. Genetic improvement for nutritive traits in perennial ryegrass has been limited, as they are typically expensive and time-consuming to measure through conventional methods. Genomic selection is appropriate for such complex and expensive traits, enabling cost-effective prediction of breeding values using genome-wide markers. The aims of the present study were to assess the potential of genomic selection for a range of nutritive traits in a multi-population training set, and to quantify contributions of family, location and family-by-location variance components to trait variation and heritability for nutritive traits. The training set consisted of a total of 517 half-sibling (half-sib) families, from five advanced breeding populations, evaluated in two distinct New Zealand grazing environments. Autumn-harvested samples were analyzed for 18 nutritive traits and maternal parents of the half-sib families were genotyped using genotyping-by-sequencing. Significant (P < 0.05) family variance was detected for all nutritive traits and genomic heritability (h2g ) was moderate to high (0.20 to 0.74). Family-by-location interactions were significant and particularly large for water soluble carbohydrate (WSC), crude fat, phosphorus (P) and crude protein. GBLUP, KGD-GBLUP and BayesCπ genomic prediction models displayed similar predictive ability, estimated by 10-fold cross validation, for all nutritive traits with values ranging from r = 0.16 to 0.45 using phenotypes from across two locations. High predictive ability was observed for the mineral traits sulfur (0.44), sodium (0.45) and magnesium (0.45) and the lowest values were observed for P (0.16), digestibility (0.22) and high molecular weight WSC (0.23). Predictive ability estimates for most nutritive traits were retained when marker number was reduced from one million to as few as 50,000. The moderate to high predictive abilities observed suggests implementation of genomic selection is feasible for most of the nutritive traits examined.
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Affiliation(s)
- Sai Krishna Arojju
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
| | - Mingshu Cao
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
| | - M Z Zulfi Jahufer
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
| | - Brent A Barrett
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
| | - Marty J Faville
- AgResearch Ltd, Grasslands Research Centre, PB 11008, Palmerston North, New Zealand
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13
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Ballesta P, Bush D, Silva FF, Mora F. Genomic Predictions Using Low-Density SNP Markers, Pedigree and GWAS Information: A Case Study with the Non-Model Species Eucalyptus cladocalyx. PLANTS (BASEL, SWITZERLAND) 2020; 9:E99. [PMID: 31941085 PMCID: PMC7020392 DOI: 10.3390/plants9010099] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Revised: 12/20/2019] [Accepted: 01/09/2020] [Indexed: 11/16/2022]
Abstract
High-throughput genotyping techniques have enabled large-scale genomic analysis to precisely predict complex traits in many plant species. However, not all species can be well represented in commercial SNP (single nucleotide polymorphism) arrays. In this study, a high-density SNP array (60 K) developed for commercial Eucalyptus was used to genotype a breeding population of Eucalyptus cladocalyx, yielding only ~3.9 K informative SNPs. Traditional Bayesian genomic models were investigated to predict flowering, stem quality and growth traits by considering the following effects: (i) polygenic background and all informative markers (GS model) and (ii) polygenic background, QTL-genotype effects (determined by GWAS) and SNP markers that were not associated with any trait (GSq model). The estimates of pedigree-based heritability and genomic heritability varied from 0.08 to 0.34 and 0.002 to 0.5, respectively, whereas the predictive ability varied from 0.19 (GS) and 0.45 (GSq). The GSq approach outperformed GS models in terms of predictive ability when the proportion of the variance explained by the significant marker-trait associations was higher than those explained by the polygenic background and non-significant markers. This approach can be particularly useful for plant/tree species poorly represented in the high-density SNP arrays, developed for economically important species, or when high-density marker panels are not available.
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Affiliation(s)
- Paulina Ballesta
- Institute of Biological Sciences, University of Talca, 2 Norte 685, Talca 3460000, Chile;
| | - David Bush
- CSIRO–Australian Tree Seed Centre, Acton 2601, Australia;
| | - Fabyano Fonseca Silva
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa 36570-900, Brazil;
| | - Freddy Mora
- Institute of Biological Sciences, University of Talca, 2 Norte 685, Talca 3460000, Chile;
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Arojju SK, Cao M, Trolove M, Barrett BA, Inch C, Eady C, Stewart A, Faville MJ. Multi-Trait Genomic Prediction Improves Predictive Ability for Dry Matter Yield and Water-Soluble Carbohydrates in Perennial Ryegrass. FRONTIERS IN PLANT SCIENCE 2020; 11:1197. [PMID: 32849742 PMCID: PMC7426495 DOI: 10.3389/fpls.2020.01197] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 07/23/2020] [Indexed: 05/10/2023]
Abstract
In perennial ryegrass (Lolium perenne L), annual and seasonal dry matter yield (DMY) and nutritive quality of herbage are high-priority traits targeted for improvement through selective breeding. Genomic prediction (GP) has proven to be a valuable tool for improving complex traits and may be further enhanced through the use of multi-trait (MT) prediction models. In this study, we evaluated the relative performance of MT prediction models to improve predictive ability for DMY and key nutritive quality traits, using two different training populations (TP1, n = 463 and TP2, n = 517) phenotyped at multiple locations. MT models outperformed single-trait (ST) models by 24% to 59% for DMY and 67% to 105% for nutritive quality traits, such as low, high, and total WSC, when a correlated secondary trait was included in both the training and test set (MT-CV2) or in the test set alone (MT-CV3) (trait-assisted genomic selection). However, when a secondary trait was included in training set and not the test set (MT-CV1), the predictive ability was not statistically significant (p > 0.05) compared to the ST model. We evaluated the impact of training set size when using a MT-CV2 model. Using a highly correlated trait (rg = 0.88) as the secondary trait in the MT-CV2 model, there was no loss in predictive ability for DMY even when the training set was reduced to 50% of its original size. In contrast, using a weakly correlated secondary trait (rg = 0.56) in the MT-CV2 model, predictive ability began to decline when the training set size was reduced by only 11% from its original size. Using a ST model, genomic predictive ability in a population unrelated to the training set was poor (rp = -0.06). However, when using an MT-CV2 model, the predictive ability was positive and high (rp = 0.76) for the same population. Our results demonstrate the first assessment of MT models in forage species and illustrate the prospects of using MT genomic selection in forages, and other outcrossing plant species, to accelerate genetic gains for complex agronomical traits, such as DMY and nutritive quality characteristics.
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Affiliation(s)
- Sai Krishna Arojju
- Grasslands Research Centre, AgResearch Ltd, Palmerston North, New Zealand
- *Correspondence: Sai Krishna Arojju,
| | - Mingshu Cao
- Grasslands Research Centre, AgResearch Ltd, Palmerston North, New Zealand
| | - Michael Trolove
- Ruakura Research Centre, AgResearch Ltd, Hamilton, New Zealand
| | - Brent A. Barrett
- Grasslands Research Centre, AgResearch Ltd, Palmerston North, New Zealand
| | | | - Colin Eady
- Barenbrug Agriseeds Ltd, Christchurch, New Zealand
| | - Alan Stewart
- Kimihia Research Centre, PGG Wrightson Seeds Ltd, Christchurch, New Zealand
| | - Marty J. Faville
- Grasslands Research Centre, AgResearch Ltd, Palmerston North, New Zealand
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Harper J, De Vega J, Swain S, Heavens D, Gasior D, Thomas A, Evans C, Lovatt A, Lister S, Thorogood D, Skøt L, Hegarty M, Blackmore T, Kudrna D, Byrne S, Asp T, Powell W, Fernandez-Fuentes N, Armstead I. Integrating a newly developed BAC-based physical mapping resource for Lolium perenne with a genome-wide association study across a L. perenne European ecotype collection identifies genomic contexts associated with agriculturally important traits. ANNALS OF BOTANY 2019; 123:977-992. [PMID: 30715119 PMCID: PMC6589518 DOI: 10.1093/aob/mcy230] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 11/28/2018] [Indexed: 05/27/2023]
Abstract
BACKGROUND AND AIMS Lolium perenne (perennial ryegrass) is the most widely cultivated forage and amenity grass species in temperate areas worldwide and there is a need to understand the genetic architectures of key agricultural traits and crop characteristics that deliver wider environmental services. Our aim was to identify genomic regions associated with agriculturally important traits by integrating a bacterial artificial chromosome (BAC)-based physical map with a genome-wide association study (GWAS). METHODS BAC-based physical maps for L. perenne were constructed from ~212 000 high-information-content fingerprints using Fingerprint Contig and Linear Topology Contig software. BAC clones were associated with both BAC-end sequences and a partial minimum tiling path sequence. A panel of 716 L. perenne diploid genotypes from 90 European accessions was assessed in the field over 2 years, and genotyped using a Lolium Infinium SNP array. The GWAS was carried out using a linear mixed model implemented in TASSEL, and extended genomic regions associated with significant markers were identified through integration with the physical map. KEY RESULTS Between ~3600 and 7500 physical map contigs were derived, depending on the software and probability thresholds used, and integrated with ~35 k sequenced BAC clones to develop a resource predicted to span the majority of the L. perenne genome. From the GWAS, eight different loci were significantly associated with heading date, plant width, plant biomass and water-soluble carbohydrate accumulation, seven of which could be associated with physical map contigs. This allowed the identification of a number of candidate genes. CONCLUSIONS Combining the physical mapping resource with the GWAS has allowed us to extend the search for candidate genes across larger regions of the L. perenne genome and identified a number of interesting gene model annotations. These physical maps will aid in validating future sequence-based assemblies of the L. perenne genome.
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Affiliation(s)
- J Harper
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - J De Vega
- Earlham Institute, Norwich Research Park, Norwich, UK
| | - S Swain
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - D Heavens
- Earlham Institute, Norwich Research Park, Norwich, UK
| | - D Gasior
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - A Thomas
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - C Evans
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - A Lovatt
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - S Lister
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - D Thorogood
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - L Skøt
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - M Hegarty
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - T Blackmore
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - D Kudrna
- Arizona Genomics Institute, School of Plant Sciences, University of Arizona, Tucson, AZ, USA
| | - S Byrne
- Teagasc, Department of Crop Science, Carlow, Ireland
| | - T Asp
- Department of Molecular Biology and Genetics, Crop Genetics and Biotechnology, Aarhus University, Slagelse, Denmark
| | - W Powell
- Scotland’s Rural College, Edinburgh, UK
| | - N Fernandez-Fuentes
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - I Armstead
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
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16
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He L, Xiao J, Rashid KY, Jia G, Li P, Yao Z, Wang X, Cloutier S, You FM. Evaluation of Genomic Prediction for Pasmo Resistance in Flax. Int J Mol Sci 2019; 20:E359. [PMID: 30654497 PMCID: PMC6359301 DOI: 10.3390/ijms20020359] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Revised: 01/06/2019] [Accepted: 01/11/2019] [Indexed: 02/06/2023] Open
Abstract
Pasmo (Septoria linicola) is a fungal disease causing major losses in seed yield and quality and stem fibre quality in flax. Pasmo resistance (PR) is quantitative and has low heritability. To improve PR breeding efficiency, the accuracy of genomic prediction (GP) was evaluated using a diverse worldwide core collection of 370 accessions. Four marker sets, including three defined by 500, 134 and 67 previously identified quantitative trait loci (QTL) and one of 52,347 PR-correlated genome-wide single nucleotide polymorphisms, were used to build ridge regression best linear unbiased prediction (RR-BLUP) models using pasmo severity (PS) data collected from field experiments performed during five consecutive years. With five-fold random cross-validation, GP accuracy as high as 0.92 was obtained from the models using the 500 QTL when the average PS was used as the training dataset. GP accuracy increased with training population size, reaching values >0.9 with training population size greater than 185. Linear regression of the observed PS with the number of positive-effect QTL in accessions provided an alternative GP approach with an accuracy of 0.86. The results demonstrate the GP models based on marker information from all identified QTL and the 5-year PS average is highly effective for PR prediction.
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Affiliation(s)
- Liqiang He
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada.
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Agriculture, Nanjing Agricultural University/JiangSu Collaborative Innovation Center for Modern Crop Production, Nanjing 210095, China.
| | - Jin Xiao
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Agriculture, Nanjing Agricultural University/JiangSu Collaborative Innovation Center for Modern Crop Production, Nanjing 210095, China.
| | - Khalid Y Rashid
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB R6M 1Y5, Canada.
| | - Gaofeng Jia
- Crop Development Centre, University of Saskatchewan, Saskatoon, SK S7N 5A8, Canada.
| | - Pingchuan Li
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB R6M 1Y5, Canada.
| | - Zhen Yao
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB R6M 1Y5, Canada.
| | - Xiue Wang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Agriculture, Nanjing Agricultural University/JiangSu Collaborative Innovation Center for Modern Crop Production, Nanjing 210095, China.
| | - Sylvie Cloutier
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada.
| | - Frank M You
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada.
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Agriculture, Nanjing Agricultural University/JiangSu Collaborative Innovation Center for Modern Crop Production, Nanjing 210095, China.
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Genomic Prediction of Growth and Stem Quality Traits in Eucalyptus globulus Labill. at Its Southernmost Distribution Limit in Chile. FORESTS 2018. [DOI: 10.3390/f9120779] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The present study was undertaken to examine the ability of different genomic selection (GS) models to predict growth traits (diameter at breast height, tree height and wood volume), stem straightness and branching quality of Eucalyptus globulus Labill. trees using a genome-wide Single Nucleotide Polymorphism (SNP) chip (60 K), in one of the southernmost progeny trials of the species, close to its southern distribution limit in Chile. The GS methods examined were Ridge Regression-BLUP (RRBLUP), Bayes-A, Bayes-B, Bayesian least absolute shrinkage and selection operator (BLASSO), principal component regression (PCR), supervised PCR and a variant of the RRBLUP method that involves the previous selection of predictor variables (RRBLUP-B). RRBLUP-B and supervised PCR models presented the greatest predictive ability (PA), followed by the PCR method, for most of the traits studied. The highest PA was obtained for the branching quality (~0.7). For the growth traits, the maximum values of PA varied from 0.43 to 0.54, while for stem straightness, the maximum value of PA reached 0.62 (supervised PCR). The study population presented a more extended linkage disequilibrium (LD) than other populations of E. globulus previously studied. The genome-wide LD decayed rapidly within 0.76 Mbp (threshold value of r2 = 0.1). The average LD on all chromosomes was r2 = 0.09. In addition, the 0.15% of total pairs of linked SNPs were in a complete LD (r2 = 1), and the 3% had an r2 value >0.5. Genomic prediction, which is based on the reduction in dimensionality and variable selection may be a promising method, considering the early growth of the trees and the low-to-moderate values of heritability found in the traits evaluated. These findings provide new understanding of how develop novel breeding strategies for tree improvement of E. globulus at its southernmost range limit in Chile, which could represent new opportunities for forest planting that can benefit the local economy.
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