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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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Cappa EP, Chen C, Klutsch JG, Sebastian-Azcona J, Ratcliffe B, Wei X, Da Ros L, Ullah A, Liu Y, Benowicz A, Sadoway S, Mansfield SD, Erbilgin N, Thomas BR, El-Kassaby YA. Multiple-trait analyses improved the accuracy of genomic prediction and the power of genome-wide association of productivity and climate change-adaptive traits in lodgepole pine. BMC Genomics 2022; 23:536. [PMID: 35870886 PMCID: PMC9308220 DOI: 10.1186/s12864-022-08747-7] [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: 02/03/2022] [Accepted: 07/08/2022] [Indexed: 11/10/2022] Open
Abstract
Background Genomic prediction (GP) and genome-wide association (GWA) analyses are currently being employed to accelerate breeding cycles and to identify alleles or genomic regions of complex traits in forest trees species. Here, 1490 interior lodgepole pine (Pinus contorta Dougl. ex. Loud. var. latifolia Engelm) trees from four open-pollinated progeny trials were genotyped with 25,099 SNPs, and phenotyped for 15 growth, wood quality, pest resistance, drought tolerance, and defense chemical (monoterpenes) traits. The main objectives of this study were to: (1) identify genetic markers associated with these traits and determine their genetic architecture, and to compare the marker detected by single- (ST) and multiple-trait (MT) GWA models; (2) evaluate and compare the accuracy and control of bias of the genomic predictions for these traits underlying different ST and MT parametric and non-parametric GP methods. GWA, ST and MT analyses were compared using a linear transformation of genomic breeding values from the respective genomic best linear unbiased prediction (GBLUP) model. GP, ST and MT parametric and non-parametric (Reproducing Kernel Hilbert Spaces, RKHS) models were compared in terms of prediction accuracy (PA) and control of bias. Results MT-GWA analyses identified more significant associations than ST. Some SNPs showed potential pleiotropic effects. Averaging across traits, PA from the studied ST-GP models did not differ significantly from each other, with generally a slight superiority of the RKHS method. MT-GP models showed significantly higher PA (and lower bias) than the ST models, being generally the PA (bias) of the RKHS approach significantly higher (lower) than the GBLUP. Conclusions The power of GWA and the accuracy of GP were improved when MT models were used in this lodgepole pine population. Given the number of GP and GWA models fitted and the traits assessed across four progeny trials, this work has produced the most comprehensive empirical genomic study across any lodgepole pine population to date. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08747-7.
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Callister AN, Bermann M, Elms S, Bradshaw BP, Lourenco D, Brawner JT. Accounting for population structure in genomic predictions of Eucalyptus globulus. G3 GENES|GENOMES|GENETICS 2022; 12:6654591. [PMID: 35920792 PMCID: PMC9434241 DOI: 10.1093/g3journal/jkac180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 06/29/2022] [Indexed: 12/02/2022]
Abstract
Genetic groups have been widely adopted in tree breeding to account for provenance effects within pedigree-derived relationship matrices. However, provenances or genetic groups have not yet been incorporated into single-step genomic BLUP (“HBLUP”) analyses of tree populations. To quantify the impact of accounting for population structure in Eucalyptus globulus, we used HBLUP to compare breeding value predictions from models excluding base population effects and models including either fixed genetic groups or the marker-derived proxies, also known as metafounders. Full-sib families from 2 separate breeding populations were evaluated across 13 sites in the “Green Triangle” region of Australia. Gamma matrices (Γ) describing similarities among metafounders reflected the geographic distribution of populations and the origins of 2 land races were identified. Diagonal elements of Γ provided population diversity or allelic covariation estimates between 0.24 and 0.56. Genetic group solutions were strongly correlated with metafounder solutions across models and metafounder effects influenced the genetic solutions of base population parents. The accuracy, stability, dispersion, and bias of model solutions were compared using the linear regression method. Addition of genomic information increased accuracy from 0.41 to 0.47 and stability from 0.68 to 0.71, while increasing bias slightly. Dispersion was within 0.10 of the ideal value (1.0) for all models. Although inclusion of metafounders did not strongly affect accuracy or stability and had mixed effects on bias, we nevertheless recommend the incorporation of metafounders in prediction models to represent the hierarchical genetic population structure of recently domesticated populations.
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Affiliation(s)
| | - Matias Bermann
- Department of Animal and Dairy Science, University of Georgia , Athens, GA 30602, USA
| | - Stephen Elms
- HVP Plantations , Churchill, VIC 3842, Australia
| | - Ben P Bradshaw
- Australian Bluegum Plantations , Albany, WA 6330, Australia
| | - Daniela Lourenco
- Department of Animal and Dairy Science, University of Georgia , Athens, GA 30602, USA
| | - Jeremy T Brawner
- Department of Plant Pathology, University of Florida , Gainesville, FL 32611, USA
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Improving lodgepole pine genomic evaluation using spatial correlation structure and SNP selection with single-step GBLUP. Heredity (Edinb) 2022; 128:209-224. [PMID: 35181761 PMCID: PMC8986842 DOI: 10.1038/s41437-022-00508-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 01/27/2022] [Accepted: 01/28/2022] [Indexed: 01/20/2023] Open
Abstract
Modeling environmental spatial heterogeneity can improve the efficiency of forest tree genomic evaluation. Furthermore, genotyping costs can be lowered by reducing the number of markers needed. We investigated the impact on variance components, breeding value accuracy, and bias of two phenotypic data adjustments (experimental design and autoregressive spatial models), and a relationship matrix calculated from a subset of markers selected for their ability to infer ancestry. Using a multiple-trait multiple-site single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) approach, four scenarios (2 phenotype adjustments × 2 marker sets) were applied to diameter at breast height (DBH), height (HT), and resistance to western gall rust (WGR) in four open-pollinated progeny trials of lodgepole pine, with 1490 (out of 11,188) trees genotyped with 25,099 SNPs. As a control, we fitted the conventional ABLUP model using pedigree information. The highest heritability estimates were achieved for the ABLUP followed closely by the ssGBLUP with the full marker set and using the spatial phenotype adjustments. The highest predictive ability was obtained by using a reduced marker subset (8000 SNPs) when either the spatial (DBH: 0.429, and WGR: 0.513) or design (HT: 0.467) phenotype corrections were used. No significant difference was detected in prediction bias among the six fitted models, and all values were close to 1 (0.918-1.014). Results demonstrated that selecting informative markers, such as those capturing ancestry, can improve the predictive ability. The use of spatial correlation structure increased traits' heritability and reduced prediction bias, while increases in predictive ability were trait-dependent.
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Ahmar S, Ballesta P, Ali M, Mora-Poblete F. Achievements and Challenges of Genomics-Assisted Breeding in Forest Trees: From Marker-Assisted Selection to Genome Editing. Int J Mol Sci 2021; 22:10583. [PMID: 34638922 PMCID: PMC8508745 DOI: 10.3390/ijms221910583] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 09/26/2021] [Accepted: 09/27/2021] [Indexed: 12/23/2022] Open
Abstract
Forest tree breeding efforts have focused mainly on improving traits of economic importance, selecting trees suited to new environments or generating trees that are more resilient to biotic and abiotic stressors. This review describes various methods of forest tree selection assisted by genomics and the main technological challenges and achievements in research at the genomic level. Due to the long rotation time of a forest plantation and the resulting long generation times necessary to complete a breeding cycle, the use of advanced techniques with traditional breeding have been necessary, allowing the use of more precise methods for determining the genetic architecture of traits of interest, such as genome-wide association studies (GWASs) and genomic selection (GS). In this sense, main factors that determine the accuracy of genomic prediction models are also addressed. In turn, the introduction of genome editing opens the door to new possibilities in forest trees and especially clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR/Cas9). It is a highly efficient and effective genome editing technique that has been used to effectively implement targetable changes at specific places in the genome of a forest tree. In this sense, forest trees still lack a transformation method and an inefficient number of genotypes for CRISPR/Cas9. This challenge could be addressed with the use of the newly developing technique GRF-GIF with speed breeding.
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Affiliation(s)
- Sunny Ahmar
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3460000, Chile;
| | - Paulina Ballesta
- The National Fund for Scientific and Technological Development, Av. del Agua 3895, Talca 3460000, Chile
| | - Mohsin Ali
- Department of Forestry and Range Management, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan;
| | - Freddy Mora-Poblete
- Institute of Biological Sciences, University of Talca, 1 Poniente 1141, Talca 3460000, Chile;
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Callister AN, Bradshaw BP, Elms S, Gillies RAW, Sasse JM, Brawner JT. Single-step genomic BLUP enables joint analysis of disconnected breeding programs: an example with Eucalyptus globulus Labill. G3-GENES GENOMES GENETICS 2021; 11:6322958. [PMID: 34568915 PMCID: PMC8473980 DOI: 10.1093/g3journal/jkab253] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 07/08/2021] [Indexed: 11/13/2022]
Abstract
Single-step GBLUP (HBLUP) efficiently combines genomic, pedigree, and phenotypic information for holistic genetic analyses of disjunct breeding populations. We combined data from two independent multigenerational Eucalyptus globulus breeding populations to provide direct comparisons across the programs and indirect predictions in environments where pedigreed families had not been evaluated. Despite few known pedigree connections between the programs, genomic relationships provided the connectivity required to create a unified relationship matrix, H, which was used to compare pedigree-based and HBLUP models. Stem volume data from 48 sites spread across three regions of southern Australia and wood quality data across 20 sites provided comparisons of model accuracy. Genotyping proved valuable for correcting pedigree errors and HBLUP more precisely defines relationships within and among populations, with relationships among the genotyped individuals used to connect the pedigrees of the two programs. Cryptic relationships among the native range populations provided evidence of population structure and evidence of the origin of landrace populations. HBLUP across programs improved the prediction accuracy of parents and genotyped individuals and enabled breeding value predictions to be directly compared and inferred in regions where little to no testing has been undertaken. The impact of incorporating genetic groups in the estimation of H will further align traditional genetic evaluation pipelines with approaches that incorporate marker-derived relationships into prediction models.
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Affiliation(s)
| | - Ben P Bradshaw
- Australian Bluegum Plantations, Albany, WA 6330, Australia
| | | | | | - Joanna M Sasse
- Sassafras Group Pty Ltd, Yarraville, VIC 3013, Australia
| | - Jeremy T Brawner
- Plant Pathology, University of Florida, Gainesville, FL 32611, USA
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Calleja-Rodriguez A, Pan J, Funda T, Chen Z, Baison J, Isik F, Abrahamsson S, Wu HX. Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine. BMC Genomics 2020; 21:796. [PMID: 33198692 PMCID: PMC7667760 DOI: 10.1186/s12864-020-07188-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 10/26/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Genomic selection (GS) or genomic prediction is a promising approach for tree breeding to obtain higher genetic gains by shortening time of progeny testing in breeding programs. As proof-of-concept for Scots pine (Pinus sylvestris L.), a genomic prediction study was conducted with 694 individuals representing 183 full-sib families that were genotyped with genotyping-by-sequencing (GBS) and phenotyped for growth and wood quality traits. 8719 SNPs were used to compare different genomic with pedigree prediction models. Additionally, four prediction efficiency methods were used to evaluate the impact of genomic breeding value estimations by assigning diverse ratios of training and validation sets, as well as several subsets of SNP markers. RESULTS Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian Ridge Regression (BRR) combined with expectation maximization (EM) imputation algorithm showed slightly higher prediction efficiencies than Pedigree Best Linear Unbiased Prediction (PBLUP) and Bayesian LASSO, with some exceptions. A subset of approximately 6000 SNP markers, was enough to provide similar prediction efficiencies as the full set of 8719 markers. Additionally, prediction efficiencies of genomic models were enough to achieve a higher selection response, that varied between 50-143% higher than the traditional pedigree-based selection. CONCLUSIONS Although prediction efficiencies were similar for genomic and pedigree models, the relative selection response was doubled for genomic models by assuming that earlier selections can be done at the seedling stage, reducing the progeny testing time, thus shortening the breeding cycle length roughly by 50%.
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Affiliation(s)
- Ainhoa Calleja-Rodriguez
- Skogforsk (The Forestry Research Institute of Sweden), Box 3, Sävar, SE 918 21 Sweden
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Umeå, SE - 901 83 Sweden
| | - Jin Pan
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Umeå, SE - 901 83 Sweden
| | - Tomas Funda
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Umeå, SE - 901 83 Sweden
- Department of Genetics and Breeding, Faculty of Agrobiology and Natural Resources, Czech University of Life Sciences Prague, Prague, 165 00 Czech Republic
- Key Laboratory of Forest Genetics and Biotechnology, Nanjing Forestry University, Nanjing, 210037 China
| | - Zhiqiang Chen
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Umeå, SE - 901 83 Sweden
| | - John Baison
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Umeå, SE - 901 83 Sweden
- RAGT Seeds, Essex, CB 101TA United Kingdom
| | - Fikret Isik
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695 USA
| | - Sara Abrahamsson
- Skogforsk (The Forestry Research Institute of Sweden), Box 3, Sävar, SE 918 21 Sweden
| | - Harry X. Wu
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Umeå, SE - 901 83 Sweden
- Beijing Advanced Innovation Centre for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, 100083 China
- National Research Collection Australia, CSIRO, Canberra, ACT 2601 Australia
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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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Klápště J, Dungey HS, Graham NJ, Telfer EJ. Effect of trait's expression level on single-step genomic evaluation of resistance to Dothistroma needle blight. BMC PLANT BIOLOGY 2020; 20:205. [PMID: 32393229 DOI: 10.1186/s12870-12020-02403-12876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 04/23/2020] [Indexed: 05/28/2023]
Abstract
BACKGROUND Many conifer breeding programs are paying increasing attention to breeding for resistance to needle disease due to the increasing importance of climate change. Phenotyping of traits related to resistance has many biological and temporal constraints that can often confound the ability to achieve reliable phenotypes and consequently, reliable genetic progress. The development of next generation sequencing platforms has also enabled implementation of genomic approaches in species lacking robust reference genomes. Genomic selection is, therefore, a promising strategy to overcome the constraints of needle disease phenotyping. RESULTS We found high accuracy in the prediction of genomic breeding values in the disease-related traits that were well characterized, reaching 0.975 for genotyped individuals and 0.587 for non-genotyped individuals. This compared well with pedigree-based accuracies of up to 0.746. Surprisingly, poorly phenotyped disease traits also showed very high accuracy in terms of correlation of predicted genomic breeding values with pedigree-based counterparts. However, this was likely caused by the fact that both were clustered around the population mean, while deviations from the population mean caused by genetic effects did not appear to be well described. Caution should therefore be taken with the interpretation of results in poorly phenotyped traits. CONCLUSIONS Implementation of genomic selection in this test population of Pinus radiata resulted in a relatively high prediction accuracy of needle loss due to Dothistroma septosporum compared with a pedigree-based approach. Using genomics to avoid biological/temporal constraints where phenotyping is reliable appears promising. Unsurprisingly, reliable phenotyping, resulting in good heritability estimates, is a fundamental requirement for the development of a reliable prediction model. Furthermore, our results are also specific to the single pathogen mating-type that is present in New Zealand, and may change with future incursion of other pathogen varieties. There is no doubt, however, that once a robust genomic prediction model is built, it will be invaluable to not only select for host tolerance, but for other economically important traits simultaneously. This tool will thus future-proof our forests by mitigating the risk of disease outbreaks induced by future changes in climate.
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Affiliation(s)
- Jaroslav Klápště
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010, New Zealand.
| | - Heidi S Dungey
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010, New Zealand
| | - Natalie J Graham
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010, New Zealand
| | - Emily J Telfer
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010, New Zealand
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Klápště J, Dungey HS, Graham NJ, Telfer EJ. Effect of trait's expression level on single-step genomic evaluation of resistance to Dothistroma needle blight. BMC PLANT BIOLOGY 2020; 20:205. [PMID: 32393229 PMCID: PMC7216529 DOI: 10.1186/s12870-020-02403-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 04/23/2020] [Indexed: 05/23/2023]
Abstract
BACKGROUND Many conifer breeding programs are paying increasing attention to breeding for resistance to needle disease due to the increasing importance of climate change. Phenotyping of traits related to resistance has many biological and temporal constraints that can often confound the ability to achieve reliable phenotypes and consequently, reliable genetic progress. The development of next generation sequencing platforms has also enabled implementation of genomic approaches in species lacking robust reference genomes. Genomic selection is, therefore, a promising strategy to overcome the constraints of needle disease phenotyping. RESULTS We found high accuracy in the prediction of genomic breeding values in the disease-related traits that were well characterized, reaching 0.975 for genotyped individuals and 0.587 for non-genotyped individuals. This compared well with pedigree-based accuracies of up to 0.746. Surprisingly, poorly phenotyped disease traits also showed very high accuracy in terms of correlation of predicted genomic breeding values with pedigree-based counterparts. However, this was likely caused by the fact that both were clustered around the population mean, while deviations from the population mean caused by genetic effects did not appear to be well described. Caution should therefore be taken with the interpretation of results in poorly phenotyped traits. CONCLUSIONS Implementation of genomic selection in this test population of Pinus radiata resulted in a relatively high prediction accuracy of needle loss due to Dothistroma septosporum compared with a pedigree-based approach. Using genomics to avoid biological/temporal constraints where phenotyping is reliable appears promising. Unsurprisingly, reliable phenotyping, resulting in good heritability estimates, is a fundamental requirement for the development of a reliable prediction model. Furthermore, our results are also specific to the single pathogen mating-type that is present in New Zealand, and may change with future incursion of other pathogen varieties. There is no doubt, however, that once a robust genomic prediction model is built, it will be invaluable to not only select for host tolerance, but for other economically important traits simultaneously. This tool will thus future-proof our forests by mitigating the risk of disease outbreaks induced by future changes in climate.
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Affiliation(s)
- Jaroslav Klápště
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010 New Zealand
| | - Heidi S. Dungey
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010 New Zealand
| | - Natalie J. Graham
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010 New Zealand
| | - Emily J. Telfer
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010 New Zealand
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Potential of Genome-Wide Association Studies and Genomic Selection to Improve Productivity and Quality of Commercial Timber Species in Tropical Rainforest, a Case Study of Shorea platyclados. FORESTS 2020. [DOI: 10.3390/f11020239] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Shorea platyclados (Dark Red Meranti) is a commercially important timber tree species in Southeast Asia. However, its stocks have dramatically declined due, inter alia, to excessive logging, insufficient natural regeneration and a slow recovery rate. Thus, there is a need to promote enrichment planting and develop effective technique to support its rehabilitation and improve timber production through implementation of Genome-Wide Association Studies (GWAS) and Genomic Selection (GS). To assist such efforts, plant materials were collected from a half-sib progeny population in Sari Bumi Kusuma forest concession, Kalimantan, Indonesia. Using 5900 markers in sequences obtained from 356 individuals, we detected high linkage disequilibrium (LD) extending up to >145 kb, suggesting that associations between phenotypic traits and markers in LD can be more easily and feasibly detected with GWAS than with analysis of quantitative trait loci (QTLs). However, the detection power of GWAS seems low, since few single nucleotide polymorphisms linked to any focal traits were detected with a stringent false discovery rate, indicating that the species’ phenotypic traits are mostly under polygenic quantitative control. Furthermore, Machine Learning provided higher prediction accuracies than Bayesian methods. We also found that stem diameter, branch diameter ratio and wood density were more predictable than height, clear bole, branch angle and wood stiffness traits. Our study suggests that GS has potential for improving the productivity and quality of S. platyclados, and our genomic heritability estimates may improve the selection of traits to target in future breeding of this species.
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Lenz PRN, Nadeau S, Mottet M, Perron M, Isabel N, Beaulieu J, Bousquet J. Multi-trait genomic selection for weevil resistance, growth, and wood quality in Norway spruce. Evol Appl 2020; 13:76-94. [PMID: 31892945 PMCID: PMC6935592 DOI: 10.1111/eva.12823] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 04/18/2019] [Accepted: 05/15/2019] [Indexed: 12/12/2022] Open
Abstract
Plantation-grown trees have to cope with an increasing pressure of pest and disease in the context of climate change, and breeding approaches using genomics may offer efficient and flexible tools to face this pressure. In the present study, we targeted genetic improvement of resistance of an introduced conifer species in Canada, Norway spruce (Picea abies (L.) Karst.), to the native white pine weevil (Pissodes strobi Peck). We developed single- and multi-trait genomic selection (GS) models and selection indices considering the relationships between weevil resistance, intrinsic wood quality, and growth traits. Weevil resistance, acoustic velocity as a proxy for mechanical wood stiffness, and average wood density showed moderate-to-high heritability and low genotype-by-environment interactions. Weevil resistance was genetically positively correlated with tree height, height-to-diameter at breast height (DBH) ratio, and acoustic velocity. The accuracy of the different GS models tested (GBLUP, threshold GBLUP, Bayesian ridge regression, BayesCπ) was high and did not differ among each other. Multi-trait models performed similarly as single-trait models when all trees were phenotyped. However, when weevil attack data were not available for all trees, weevil resistance was more accurately predicted by integrating genetically correlated growth traits into multi-trait GS models. A GS index that corresponded to the breeders' priorities achieved near maximum gains for weevil resistance, acoustic velocity, and height growth, but a small decrease for DBH. The results of this study indicate that it is possible to breed for high-quality, weevil-resistant Norway spruce reforestation stock with high accuracy achieved from single-trait or multi-trait GS.
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Affiliation(s)
- Patrick R. N. Lenz
- Canadian Wood Fibre CentreNatural Resources CanadaQuébecQuébecCanada
- Canada Research Chair in Forest GenomicsInstitute of Integrative Biology and Systems, Centre for Forest ResearchUniversité LavalQuébecQuébecCanada
| | - Simon Nadeau
- Canadian Wood Fibre CentreNatural Resources CanadaQuébecQuébecCanada
| | - Marie‐Josée Mottet
- Ministère des Forêts, de la Faune et des ParcsGouvernement du Québec, Direction de la recherche forestièreQuébecQuébecCanada
| | - Martin Perron
- Canada Research Chair in Forest GenomicsInstitute of Integrative Biology and Systems, Centre for Forest ResearchUniversité LavalQuébecQuébecCanada
- Ministère des Forêts, de la Faune et des ParcsGouvernement du Québec, Direction de la recherche forestièreQuébecQuébecCanada
| | - Nathalie Isabel
- Canada Research Chair in Forest GenomicsInstitute of Integrative Biology and Systems, Centre for Forest ResearchUniversité LavalQuébecQuébecCanada
- Laurentian Forestry CentreNatural Resources CanadaQuébecQuébecCanada
| | - Jean Beaulieu
- Canada Research Chair in Forest GenomicsInstitute of Integrative Biology and Systems, Centre for Forest ResearchUniversité LavalQuébecQuébecCanada
| | - Jean Bousquet
- Canada Research Chair in Forest GenomicsInstitute of Integrative Biology and Systems, Centre for Forest ResearchUniversité LavalQuébecQuébecCanada
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Imai A, Kuniga T, Yoshioka T, Nonaka K, Mitani N, Fukamachi H, Hiehata N, Yamamoto M, Hayashi T. Single-step genomic prediction of fruit-quality traits using phenotypic records of non-genotyped relatives in citrus. PLoS One 2019; 14:e0221880. [PMID: 31465502 PMCID: PMC6715226 DOI: 10.1371/journal.pone.0221880] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 08/17/2019] [Indexed: 11/24/2022] Open
Abstract
The potential of genomic selection (GS) is currently being evaluated for fruit breeding. GS models are usually constructed based on information from both the genotype and phenotype of population. However, information from phenotyped but non-genotyped relatives can also be used to construct GS models, and this additional information can improve their accuracy. In the present study, we evaluated the utility of single-step genomic best linear unbiased prediction (ssGBLUP) in citrus breeding, which is a genomic prediction method that combines the kinship information from genotyped and non-genotyped relatives into a single relationship matrix for a mixed model to apply GS. Fruit weight, sugar content, and acid content of 1,935 citrus individuals, of which 483 had genotype data of 2,354 genome-wide single nucleotide polymorphisms, were evaluated from 2009–2012. The prediction accuracy of ssGBLUP for genotyped individuals was similar to or higher than that of usual genomic best linear unbiased prediction method using only genotyped individuals, especially for sugar content. Therefore, ssGBLUP could yield higher accuracy in genotyped individuals by adding information from non-genotyped relatives. The prediction accuracy of ssGBLUP for non-genotyped individuals was also slightly higher than that of conventional best linear unbiased prediction method using pedigree information. This indicates that ssGBLUP can enhance prediction accuracy of breeding values for non-genotyped individuals using genomic information of genotyped relatives. These results demonstrate the potential of ssGBLUP for fruit breeding, including citrus.
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Affiliation(s)
- Atsushi Imai
- Institute of Fruit Tree and Tea Science, National Agriculture and Food Research Organization, Fujimoto, Tsukuba, Ibaraki, Japan
- Graduate School of Life and Environmental Science, University of Tsukuba, Tennodai, Tsukuba, Ibaraki, Japan
| | - Takeshi Kuniga
- Western Region Agricultural Research Center, National Agriculture and Food Research Organization, Senyucho, Zentsuji, Kagawa, Japan
| | - Terutaka Yoshioka
- Western Region Agricultural Research Center, National Agriculture and Food Research Organization, Senyucho, Zentsuji, Kagawa, Japan
| | - Keisuke Nonaka
- Institute of Fruit Tree and Tea Science, National Agriculture and Food Research Organization, Okitsunakacho, Shimizu, Shizuoka, Japan
| | - Nobuhito Mitani
- Institute of Fruit Tree and Tea Science, National Agriculture and Food Research Organization, Fujimoto, Tsukuba, Ibaraki, Japan
| | - Hiroshi Fukamachi
- Institute of Fruit Tree and Tea Science, National Agriculture and Food Research Organization, Okitsunakacho, Shimizu, Shizuoka, Japan
| | - Naofumi Hiehata
- Nagasaki Agricultural and Forestry Technical Development Center, Nagasaki Prefectural Government, Kaizumachi, Isahaya, Nagasaki, Japan
| | - Masashi Yamamoto
- Faculty of Agriculture, Kagoshima University, Korimoto, Kagoshima, Kagoshima, Japan
| | - Takeshi Hayashi
- Graduate School of Life and Environmental Science, University of Tsukuba, Tennodai, Tsukuba, Ibaraki, Japan
- Institute of Crop Science, National Agriculture and Food Research Organization, Kannondai, Tsukuba, Ibaraki, Japan
- * E-mail:
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14
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Grattapaglia D, Silva-Junior OB, Resende RT, Cappa EP, Müller BSF, Tan B, Isik F, Ratcliffe B, El-Kassaby YA. Quantitative Genetics and Genomics Converge to Accelerate Forest Tree Breeding. FRONTIERS IN PLANT SCIENCE 2018; 9:1693. [PMID: 30524463 PMCID: PMC6262028 DOI: 10.3389/fpls.2018.01693] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 10/31/2018] [Indexed: 05/18/2023]
Abstract
Forest tree breeding has been successful at delivering genetically improved material for multiple traits based on recurrent cycles of selection, mating, and testing. However, long breeding cycles, late flowering, variable juvenile-mature correlations, emerging pests and diseases, climate, and market changes, all pose formidable challenges. Genetic dissection approaches such as quantitative trait mapping and association genetics have been fruitless to effectively drive operational marker-assisted selection (MAS) in forest trees, largely because of the complex multifactorial inheritance of most, if not all traits of interest. The convergence of high-throughput genomics and quantitative genetics has established two new paradigms that are changing contemporary tree breeding dogmas. Genomic selection (GS) uses large number of genome-wide markers to predict complex phenotypes. It has the potential to accelerate breeding cycles, increase selection intensity and improve the accuracy of breeding values. Realized genomic relationships matrices, on the other hand, provide innovations in genetic parameters' estimation and breeding approaches by tracking the variation arising from random Mendelian segregation in pedigrees. In light of a recent flow of promising experimental results, here we briefly review the main concepts, analytical tools and remaining challenges that currently underlie the application of genomics data to tree breeding. With easy and cost-effective genotyping, we are now at the brink of extensive adoption of GS in tree breeding. Areas for future GS research include optimizing strategies for updating prediction models, adding validated functional genomics data to improve prediction accuracy, and integrating genomic and multi-environment data for forecasting the performance of genetic material in untested sites or under changing climate scenarios. The buildup of phenotypic and genome-wide data across large-scale breeding populations and advances in computational prediction of discrete genomic features should also provide opportunities to enhance the application of genomics to tree breeding.
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Affiliation(s)
- Dario Grattapaglia
- EMBRAPA Recursos Genéticos e BiotecnologiaBrasília, Brazil
- Programa de Ciências Genômicas e BiotecnologiaUniversidade Católica de Brasília, Brasília, Brazil
- Departamento de Biologia CelularUniversidade de Brasília, Brasília, Brazil
- Department of Forestry and Environmental Resources, North Carolina State UniversityRaleigh, NC, United States
| | - Orzenil B. Silva-Junior
- EMBRAPA Recursos Genéticos e BiotecnologiaBrasília, Brazil
- Programa de Ciências Genômicas e BiotecnologiaUniversidade Católica de Brasília, Brasília, Brazil
| | | | - Eduardo P. Cappa
- Centro de Investigación de Recursos Naturales, Instituto de Recursos BiológicosINTA, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y TécnicasBuenos Aires, Argentina
| | - Bárbara S. F. Müller
- EMBRAPA Recursos Genéticos e BiotecnologiaBrasília, Brazil
- Departamento de Biologia CelularUniversidade de Brasília, Brasília, Brazil
| | - Biyue Tan
- Biomaterials DivisionStora Enso AB, Stockholm, Sweden
| | - Fikret Isik
- Department of Forestry and Environmental Resources, North Carolina State UniversityRaleigh, NC, United States
| | - Blaise Ratcliffe
- Department of Forest and Conservation Sciences, Faculty of Forestry, University of British ColumbiaVancouver, BC, Canada
| | - Yousry A. El-Kassaby
- Department of Forest and Conservation Sciences, Faculty of Forestry, University of British ColumbiaVancouver, BC, Canada
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15
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Klápště J, Suontama M, Dungey HS, Telfer EJ, Graham NJ, Low CB, Stovold GT. Effect of Hidden Relatedness on Single-Step Genetic Evaluation in an Advanced Open-Pollinated Breeding Program. J Hered 2018; 109:802-810. [PMID: 30285150 PMCID: PMC6208454 DOI: 10.1093/jhered/esy051] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Accepted: 09/27/2018] [Indexed: 01/17/2023] Open
Abstract
Open-pollinated (OP) mating is frequently used in forest tree breeding due to the relative temporal and financial efficiency of the approach. The trade-off is the lower precision of the estimated genetic parameters. Pedigree/sib-ship reconstruction has been proven as a tool to correct and complete pedigree information and to improve the precision of genetic parameter estimates. Our study analyzed an advanced generation Eucalyptus population from an OP breeding program using single-step genetic evaluation. The relationship matrix inferred from sib-ship reconstruction was used to rescale the marker-based relationship matrix (G matrix). This was compared with a second scenario that used rescaling based on the documented pedigree. The proposed single-step model performed better with respect to both model fit and the theoretical accuracy of breeding values. We found that the prediction accuracy was superior when using the pedigree information only when compared with using a combination of the pedigree and genomic information. This pattern appeared to be mainly a result of accumulated unrecognized relatedness over several breeding cycles, resulting in breeding values being shrunk toward the population mean. Using biased, pedigree-based breeding values as the base with which to correlate predicted GEBVs, resulted in the underestimation of prediction accuracies. Using breeding values estimated on the basis of sib-ship reconstruction resulted in increased prediction accuracies of the genotyped individuals. Therefore, selection of the correct base for estimation of prediction accuracy is critical. The beneficial impact of sib-ship reconstruction using G matrix rescaling was profound, especially in traits with inbreeding depression, such as stem diameter.
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Affiliation(s)
- Jaroslav Klápště
- Scion (New Zealand Forest Research Institute Ltd.), Whakarewarewa, Rotorua, New Zealand. Mari Suontama is now at Skogforsk, Umeå, Sävar SE, Sweden
| | - Mari Suontama
- Scion (New Zealand Forest Research Institute Ltd.), Whakarewarewa, Rotorua, New Zealand. Mari Suontama is now at Skogforsk, Umeå, Sävar SE, Sweden
| | - Heidi S Dungey
- Scion (New Zealand Forest Research Institute Ltd.), Whakarewarewa, Rotorua, New Zealand. Mari Suontama is now at Skogforsk, Umeå, Sävar SE, Sweden
| | - Emily J Telfer
- Scion (New Zealand Forest Research Institute Ltd.), Whakarewarewa, Rotorua, New Zealand. Mari Suontama is now at Skogforsk, Umeå, Sävar SE, Sweden
| | - Natalie J Graham
- Scion (New Zealand Forest Research Institute Ltd.), Whakarewarewa, Rotorua, New Zealand. Mari Suontama is now at Skogforsk, Umeå, Sävar SE, Sweden
| | - Charlie B Low
- Scion (New Zealand Forest Research Institute Ltd.), Whakarewarewa, Rotorua, New Zealand. Mari Suontama is now at Skogforsk, Umeå, Sävar SE, Sweden
| | - Grahame T Stovold
- Scion (New Zealand Forest Research Institute Ltd.), Whakarewarewa, Rotorua, New Zealand. Mari Suontama is now at Skogforsk, Umeå, Sävar SE, Sweden
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