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Kang HI, Kim IS, Shim D, Kang KS, Cheon KS. Genomic selection for growth characteristics in Korean red pine ( Pinus densiflora Seibold & Zucc.). FRONTIERS IN PLANT SCIENCE 2024; 15:1285094. [PMID: 38322820 PMCID: PMC10844423 DOI: 10.3389/fpls.2024.1285094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 01/05/2024] [Indexed: 02/08/2024]
Abstract
Traditionally, selective breeding has been used to improve tree growth. However, traditional selection methods are time-consuming and limit annual genetic gain. Genomic selection (GS) offers an alternative to progeny testing by estimating the genotype-based breeding values of individuals based on genomic information using molecular markers. In the present study, we introduced GS to an open-pollinated breeding population of Korean red pine (Pinus densiflora), which is in high demand in South Korea, to shorten the breeding cycle. We compared the prediction accuracies of GS for growth characteristics (diameter at breast height [DBH], height, straightness, and volume) in Korean red pines under various conditions (marker set, model, and training set) and evaluated the selection efficiency of GS compared to traditional selection methods. Training the GS model to include individuals from various environments using genomic best linear unbiased prediction (GBLUP) and markers with a minor allele frequency larger than 0.05 was effective. The optimized model had an accuracy of 0.164-0.498 and a predictive ability of 0.018-0.441. The predictive ability of GBLUP against that of additive best linear unbiased prediction (ABLUP) was 0.86-5.10, and against the square root of heritability was 0.19-0.76, indicating that GS for Korean red pine was as efficient as in previous studies on forest trees. Moreover, the response to GS was higher than that to traditional selection regarding the annual genetic gain. Therefore, we conclude that the trained GS model is more effective than the traditional breeding methods for Korean red pines. We anticipate that the next generation of trees selected by GS will lay the foundation for the accelerated breeding of Korean red pine.
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Affiliation(s)
- Hye-In Kang
- Division of Tree Improvement and Biotechnology, Department of Forest Bio-resources, National Institute of Forest Science, Suwon, Republic of Korea
| | - In Sik Kim
- Division of Tree Improvement and Biotechnology, Department of Forest Bio-resources, National Institute of Forest Science, Suwon, Republic of Korea
| | - Donghwan Shim
- Department of Biological Sciences, Chungnam National University, Daejeon, Republic of Korea
| | - Kyu-Suk Kang
- Department of Agriculture, Forestry and Bioresources, College of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea
| | - Kyeong-Seong Cheon
- Division of Tree Improvement and Biotechnology, Department of Forest Bio-resources, National Institute of Forest Science, Suwon, Republic of Korea
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Gebremedhin A, Li Y, Shunmugam ASK, Sudheesh S, Valipour-Kahrood H, Hayden MJ, Rosewarne GM, Kaur S. Genomic selection for target traits in the Australian lentil breeding program. FRONTIERS IN PLANT SCIENCE 2024; 14:1284781. [PMID: 38235201 PMCID: PMC10791954 DOI: 10.3389/fpls.2023.1284781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/07/2023] [Indexed: 01/19/2024]
Abstract
Genomic selection (GS) uses associations between markers and phenotypes to predict the breeding values of individuals. It can be applied early in the breeding cycle to reduce the cross-to-cross generation interval and thereby increase genetic gain per unit of time. The development of cost-effective, high-throughput genotyping platforms has revolutionized plant breeding programs by enabling the implementation of GS at the scale required to achieve impact. As a result, GS is becoming routine in plant breeding, even in minor crops such as pulses. Here we examined 2,081 breeding lines from Agriculture Victoria's national lentil breeding program for a range of target traits including grain yield, ascochyta blight resistance, botrytis grey mould resistance, salinity and boron stress tolerance, 100-grain weight, seed size index and protein content. A broad range of narrow-sense heritabilities was observed across these traits (0.24-0.66). Genomic prediction models were developed based on 64,781 genome-wide SNPs using Bayesian methodology and genomic estimated breeding values (GEBVs) were calculated. Forward cross-validation was applied to examine the prediction accuracy of GS for these targeted traits. The accuracy of GEBVs was consistently higher (0.34-0.83) than BLUP estimated breeding values (EBVs) (0.22-0.54), indicating a higher expected rate of genetic gain with GS. GS-led parental selection using early generation breeding materials also resulted in higher genetic gain compared to BLUP-based selection performed using later generation breeding lines. Our results show that implementing GS in lentil breeding will fast track the development of high-yielding cultivars with increased resistance to biotic and abiotic stresses, as well as improved seed quality traits.
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Affiliation(s)
- Alem Gebremedhin
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Yongjun Li
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | | | - Shimna Sudheesh
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | | | - Matthew J. Hayden
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
| | | | - Sukhjiwan Kaur
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, Australia
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Nan J, Yang S, Zhang X, Leng T, Zhuoma J, Zhuoma R, Yuan J, Pi J, Sheng Z, Li S. Identification of candidate genes related to highland adaptation from multiple Chinese local chicken breeds by whole genome sequencing analysis. Anim Genet 2023; 54:55-67. [PMID: 36305422 DOI: 10.1111/age.13268] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 08/30/2022] [Accepted: 09/20/2022] [Indexed: 01/07/2023]
Abstract
Understanding the genetic mechanism of highland adaptation is of great importance for breeding improvement of Tibetan chickens (TBC). Some studies of TBC have identified some candidate genes and pathways from multiple subgroups, but the related genetic mechanisms remain largely unknown. Different genetic backgrounds and the independent genetic basis of highland adaptation make it difficult to identity the selective region of highland adaptation with all TBC samples. In this study, we conducted pre-analysis in a large-scale population to select a TBC subgroup with the purest and highest level of highland-specific lineage for the further analysis. Finally, the 37 samples from a TBC subgroup and 19 Lahsa White chickens were used to represent the highland group for further analysis with 80 samples from five Chinese local lowland breeds as controls. Population structure analysis revealed that highland adaptation significantly affected population stratification in Chinese local chicken breeds. Genome-wide selection signal analysis identified 201 candidate genes associated with highland adaptation of TBC, and these genes were significantly enriched in calcium signaling, vascular smooth muscle contraction and the cellular response to oxidative stress pathways. Additionally, we identified a narrow 1.76 kb region containing an overlapping region between HBZ and an active enhancer, and our identified region showed a highly significant signal. The highland group selected the haplotype with high activity to improve the oxygen-carrying capacity, thus being adapted to a hypoxic environment. We also found that STX2 was significantly selected in the highland group, thus potentially reducing the oxidative stress caused by hypoxia, and that STX2 exhibited the opposite effects on highland adaptation and reproductive traits. Our findings advance our understanding of extreme environment adaptation of highland chickens, and provide some variants and genes beneficial to TBC genetic breeding improvement.
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Affiliation(s)
- Jiuhong Nan
- State Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Sendong Yang
- State Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Xiaojian Zhang
- State Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Tianze Leng
- State Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Marine Ecosystem Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China
| | - Joan Zhuoma
- State Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Neighborhood Committee Office, Xigaze City, China
| | - Rensang Zhuoma
- State Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Luomai Township People's Government of Seni District, Naqu City, China
| | - Jingwei Yuan
- Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Jinsong Pi
- Institute of Animal Husbandry and Veterinary, Hubei Academy of Agricultural Science, Wuhan, China
| | - Zheya Sheng
- State Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Shijun Li
- State Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China.,Key Laboratory of Smart Farming for Agricultural Animals, Ministry of Education, Huazhong Agricultural University, Wuhan, China.,Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
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Freeman JS, Slavov GT, Butler JB, Frickey T, Graham NJ, Klápště J, Lee J, Telfer EJ, Wilcox P, Dungey HS. High density linkage maps, genetic architecture, and genomic prediction of growth and wood properties in Pinus radiata. BMC Genomics 2022; 23:731. [PMID: 36307760 PMCID: PMC9617409 DOI: 10.1186/s12864-022-08950-6] [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: 02/21/2022] [Accepted: 10/10/2022] [Indexed: 11/19/2022] Open
Abstract
Background The growing availability of genomic resources in radiata pine paves the way for significant advances in fundamental and applied genomic research. We constructed robust high-density linkage maps based on exome-capture genotyping in two F1 populations, and used these populations to perform quantitative trait locus (QTL) scans, genomic prediction and quantitative analyses of genetic architecture for key traits targeted by tree improvement programmes. Results Our mapping approach used probabilistic error correction of the marker data, followed by an iterative approach based on stringent parameters. This approach proved highly effective in producing high-density maps with robust marker orders and realistic map lengths (1285–4674 markers per map, with sizes ranging from c. 1643–2292 cM, and mean marker intervals of 0.7–2.1 cM). Colinearity was high between parental linkage maps, although there was evidence for a large chromosomal rearrangement (affecting ~ 90 cM) in one of the parental maps. In total, 28 QTL were detected for growth (stem diameter) and wood properties (wood density and fibre properties measured by Silviscan) in the QTL discovery population, with 1–3 QTL of small to moderate effect size detected per trait in each parental map. Four of these QTL were validated in a second, unrelated F1 population. Results from genomic prediction and analyses of genetic architecture were consistent with those from QTL scans, with wood properties generally having moderate to high genomic heritabilities and predictive abilities, as well as somewhat less complex genetic architectures, compared to growth traits. Conclusions Despite the economic importance of radiata pine as a plantation forest tree, robust high-density linkage maps constructed from reproducible, sequence-anchored markers have not been published to date. The maps produced in this study will be a valuable resource for several applications, including the selection of marker panels for genomic prediction and anchoring a recently completed de novo whole genome assembly. We also provide the first map-based evidence for a large genomic rearrangement in radiata pine. Finally, results from our QTL scans, genomic prediction, and genetic architecture analyses are informative about the genomic basis of variation in important phenotypic traits. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08950-6.
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Beaulieu J, Lenz P, Bousquet J. Metadata analysis indicates biased estimation of genetic parameters and gains using conventional pedigree information instead of genomic-based approaches in tree breeding. Sci Rep 2022; 12:3933. [PMID: 35273188 PMCID: PMC8913692 DOI: 10.1038/s41598-022-06681-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 01/31/2022] [Indexed: 11/09/2022] Open
Abstract
Forest tree improvement helps provide adapted planting stock to ensure growth productivity, fibre quality and carbon sequestration through reforestation and afforestation activities. However, there is increasing doubt that conventional pedigree provides the most accurate estimates for selection and prediction of performance of improved planting stock. When the additive genetic relationships among relatives is estimated using pedigree information, it is not possible to take account of Mendelian sampling due to the random segregation of parental alleles. The use of DNA markers distributed genome-wide (multi-locus genotypes) makes it possible to estimate the realized additive genomic relationships, which takes account of the Mendelian sampling and possible pedigree errors. We reviewed a series of papers on conifer and broadleaf tree species in which both pedigree-based and marker-based estimates of genetic parameters have been reported. Using metadata analyses, we show that for heritability and genetic gains, the estimates obtained using only the pedigree information are generally biased upward compared to those obtained using DNA markers distributed genome-wide, and that genotype-by-environment (GxE) interaction can be underestimated for low to moderate heritability traits. As high-throughput genotyping becomes economically affordable, we recommend expanding the use of genomic selection to obtain more accurate estimates of genetic parameters and gains.
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Affiliation(s)
- Jean Beaulieu
- Canada Research Chair in Forest Genomics, Institute of Systems and Integrative Biology and Centre for Forest Research, Université Laval, 1030 Avenue de la Médecine, Quebec, QC, G1V 0A6, Canada.
| | - Patrick Lenz
- Canada Research Chair in Forest Genomics, Institute of Systems and Integrative Biology and Centre for Forest Research, Université Laval, 1030 Avenue de la Médecine, Quebec, QC, G1V 0A6, Canada.,Natural Resources Canada, Canadian Wood Fibre Centre, Quebec, QC, G1V 4C7, Canada
| | - Jean Bousquet
- Canada Research Chair in Forest Genomics, Institute of Systems and Integrative Biology and Centre for Forest Research, Université Laval, 1030 Avenue de la Médecine, Quebec, QC, G1V 0A6, Canada
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Development and Validation of a 36K SNP Array for Radiata Pine (Pinus radiata D.Don). FORESTS 2022. [DOI: 10.3390/f13020176] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Radiata pine (Pinus radiata D.Don) is one of the world’s most domesticated pines and a key economic species in New Zealand. Thus, the development of genomic resources for radiata pine has been a high priority for both research and commercial breeding. Leveraging off a previously developed exome capture panel, we tested the performance of 438,744 single nucleotide polymorphisms (SNPs) on a screening array (NZPRAD01) and then selected 36,285 SNPs for a final genotyping array (NZPRAD02). These SNPs aligned to 15,372 scaffolds from the Pinus taeda L. v. 1.01e assembly, and 20,039 contigs from the radiata pine transcriptome assembly. The genotyping array was tested on more than 8000 samples, including material from archival progenitors, current breeding trials, nursery material, clonal lines, and material from Australia. Our analyses indicate that the array is performing well, with sample call rates greater than 98% and a sample reproducibility of 99.9%. Genotyping in two linkage mapping families indicated that the SNPs are well distributed across the 12 linkage groups. Using genotypic data from this array, we were also able to differentiate representatives of the five recognized provenances of radiata pine, Año Nuevo, Monterey, Cambria, Cedros and Guadalupe. Furthermore, principal component analysis of genotyped trees revealed clear patterns of population structure, with the primary axis of variation driven by provenance ancestry and the secondary axis reflecting breeding activities. This represents the first commercial use of genomics in a radiata pine breeding program.
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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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8
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Yu S, Bekkering CS, Tian L. Metabolic engineering in woody plants: challenges, advances, and opportunities. ABIOTECH 2021; 2:299-313. [PMID: 36303882 PMCID: PMC9590576 DOI: 10.1007/s42994-021-00054-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 06/06/2021] [Indexed: 06/16/2023]
Abstract
Woody plant species represent an invaluable reserve of biochemical diversity to which metabolic engineering can be applied to satisfy the need for commodity and specialty chemicals, pharmaceuticals, and renewable energy. Woody plants are particularly promising for this application due to their low input needs, high biomass, and immeasurable ecosystem services. However, existing challenges have hindered their widespread adoption in metabolic engineering efforts, such as long generation times, large and highly heterozygous genomes, and difficulties in transformation and regeneration. Recent advances in omics approaches, systems biology modeling, and plant transformation and regeneration methods provide effective approaches in overcoming these outstanding challenges. Promises brought by developments in this space are steadily opening the door to widespread metabolic engineering of woody plants to meet the global need for a wide range of sustainably sourced chemicals and materials.
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Affiliation(s)
- Shu Yu
- Department of Plant Sciences, Mail Stop 3, University of California, Davis, CA 95616 USA
| | - Cody S. Bekkering
- Department of Plant Sciences, Mail Stop 3, University of California, Davis, CA 95616 USA
| | - Li Tian
- Department of Plant Sciences, Mail Stop 3, University of California, Davis, CA 95616 USA
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Jurcic EJ, Villalba PV, Pathauer PS, Palazzini DA, Oberschelp GPJ, Harrand L, Garcia MN, Aguirre NC, Acuña CV, Martínez MC, Rivas JG, Cisneros EF, López JA, Poltri SNM, Munilla S, Cappa EP. Single-step genomic prediction of Eucalyptus dunnii using different identity-by-descent and identity-by-state relationship matrices. Heredity (Edinb) 2021; 127:176-189. [PMID: 34145424 PMCID: PMC8322403 DOI: 10.1038/s41437-021-00450-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 06/07/2021] [Accepted: 06/07/2021] [Indexed: 02/05/2023] Open
Abstract
Genomic selection based on the single-step genomic best linear unbiased prediction (ssGBLUP) approach is becoming an important tool in forest tree breeding. The quality of the variance components and the predictive ability of the estimated breeding values (GEBV) depends on how well marker-based genomic relationships describe the actual genetic relationships at unobserved causal loci. We investigated the performance of GEBV obtained when fitting models with genomic covariance matrices based on two identity-by-descent (IBD) and two identity-by-state (IBS) relationship measures. Multiple-trait multiple-site ssGBLUP models were fitted to diameter and stem straightness in five open-pollinated progeny trials of Eucalyptus dunnii, genotyped using the EUChip60K. We also fitted the conventional ABLUP model with a pedigree-based covariance matrix. Estimated relationships from the IBD estimators displayed consistently lower standard deviations than those from the IBS approaches. Although ssGBLUP based in IBS estimators resulted in higher trait-site heritabilities, the gain in accuracy of the relationships using IBD estimators has resulted in higher predictive ability and lower bias of GEBV, especially for low-heritability trait-site. ssGBLUP based on IBS and IBD approaches performed considerably better than the traditional ABLUP. In summary, our results advocate the use of the ssGBLUP approach jointly with the IBD relationship matrix in open-pollinated forest tree evaluation.
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Affiliation(s)
- Esteban J Jurcic
- Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Recursos Biológicos, Centro de Investigación en Recursos Naturales, Buenos Aires, Argentina.
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
| | - Pamela V Villalba
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
- Instituto de Agrobiotecnología y Biología Molecular (IABiMo), INTA-CONICET, Buenos Aires, Argentina
| | - Pablo S Pathauer
- Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Recursos Biológicos, Centro de Investigación en Recursos Naturales, Buenos Aires, Argentina
| | - Dino A Palazzini
- Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Recursos Biológicos, Centro de Investigación en Recursos Naturales, Buenos Aires, Argentina
| | - Gustavo P J Oberschelp
- Instituto Nacional de Tecnología Agropecuaria (INTA), Estación Experimental Agropecuaria Concordia, Entre Ríos, Argentina
| | - Leonel Harrand
- Instituto Nacional de Tecnología Agropecuaria (INTA), Estación Experimental Agropecuaria Concordia, Entre Ríos, Argentina
| | - Martín N Garcia
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
- Instituto de Agrobiotecnología y Biología Molecular (IABiMo), INTA-CONICET, Buenos Aires, Argentina
| | - Natalia C Aguirre
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
- Instituto de Agrobiotecnología y Biología Molecular (IABiMo), INTA-CONICET, Buenos Aires, Argentina
| | - Cintia V Acuña
- Instituto de Agrobiotecnología y Biología Molecular (IABiMo), INTA-CONICET, Buenos Aires, Argentina
| | - María C Martínez
- Instituto de Agrobiotecnología y Biología Molecular (IABiMo), INTA-CONICET, Buenos Aires, Argentina
| | - Juan G Rivas
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
- Instituto de Agrobiotecnología y Biología Molecular (IABiMo), INTA-CONICET, Buenos Aires, Argentina
| | - Esteban F Cisneros
- Facultad de Ciencias Forestales, Universidad Nacional de Santiago del Estero, Santiago del Estero, Argentina
| | - Juan A López
- Instituto Nacional de Tecnología Agropecuaria (INTA), Estación Experimental Agropecuaria Bella Vista, Corrientes, Argentina
| | - Susana N Marcucci Poltri
- Instituto de Agrobiotecnología y Biología Molecular (IABiMo), INTA-CONICET, Buenos Aires, Argentina
| | - Sebastián Munilla
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
- Departamento de Producción Animal, Facultad de Agronomía, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Eduardo P Cappa
- Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Recursos Biológicos, Centro de Investigación en Recursos Naturales, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
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Weng Z, Yang Y, Wang X, Wu L, Hua S, Zhang H, Meng Z. Parentage Analysis in Giant Grouper ( Epinephelus lanceolatus) Using Microsatellite and SNP Markers from Genotyping-by-Sequencing Data. Genes (Basel) 2021; 12:genes12071042. [PMID: 34356058 PMCID: PMC8304347 DOI: 10.3390/genes12071042] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 07/01/2021] [Accepted: 07/02/2021] [Indexed: 12/18/2022] Open
Abstract
Pedigree information is necessary for the maintenance of diversity for wild and captive populations. Accurate pedigree is determined by molecular marker-based parentage analysis, which may be influenced by the polymorphism and number of markers, integrity of samples, relatedness of parents, or different analysis programs. Here, we described the first development of 208 single nucleotide polymorphisms (SNPs) and 11 microsatellites for giant grouper (Epinephelus lanceolatus) taking advantage of Genotyping-by-sequencing (GBS), and compared the power of SNPs and microsatellites for parentage and relatedness analysis, based on a mixed family composed of 4 candidate females, 4 candidate males and 289 offspring. CERVUS, PAPA and COLONY were used for mutually verification. We found that SNPs had a better potential for relatedness estimation, exclusion of non-parentage and individual identification than microsatellites, and > 98% accuracy of parentage assignment could be achieved by 100 polymorphic SNPs (MAF cut-off < 0.4) or 10 polymorphic microsatellites (mean Ho = 0.821, mean PIC = 0.651). This study provides a reference for the development of molecular markers for parentage analysis taking advantage of next-generation sequencing, and contributes to the molecular breeding, fishery management and population conservation.
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Affiliation(s)
- Zhuoying Weng
- State Key Laboratory of Biocontrol, Guangdong Province Key Laboratory for Aquatic Economic Animals, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510275, China; (Z.W.); (Y.Y.); (X.W.); (L.W.); (S.H.); (H.Z.)
| | - Yang Yang
- State Key Laboratory of Biocontrol, Guangdong Province Key Laboratory for Aquatic Economic Animals, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510275, China; (Z.W.); (Y.Y.); (X.W.); (L.W.); (S.H.); (H.Z.)
| | - Xi Wang
- State Key Laboratory of Biocontrol, Guangdong Province Key Laboratory for Aquatic Economic Animals, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510275, China; (Z.W.); (Y.Y.); (X.W.); (L.W.); (S.H.); (H.Z.)
| | - Lina Wu
- State Key Laboratory of Biocontrol, Guangdong Province Key Laboratory for Aquatic Economic Animals, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510275, China; (Z.W.); (Y.Y.); (X.W.); (L.W.); (S.H.); (H.Z.)
| | - Sijie Hua
- State Key Laboratory of Biocontrol, Guangdong Province Key Laboratory for Aquatic Economic Animals, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510275, China; (Z.W.); (Y.Y.); (X.W.); (L.W.); (S.H.); (H.Z.)
| | - Hanfei Zhang
- State Key Laboratory of Biocontrol, Guangdong Province Key Laboratory for Aquatic Economic Animals, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510275, China; (Z.W.); (Y.Y.); (X.W.); (L.W.); (S.H.); (H.Z.)
| | - Zining Meng
- State Key Laboratory of Biocontrol, Guangdong Province Key Laboratory for Aquatic Economic Animals, School of Life Sciences, Sun Yat-Sen University, Guangzhou 510275, China; (Z.W.); (Y.Y.); (X.W.); (L.W.); (S.H.); (H.Z.)
- Southern Laboratory of Ocean Science and Engineering, Zhuhai 519000, China
- Correspondence:
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11
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Beaulieu J, Nadeau S, Ding C, Celedon JM, Azaiez A, Ritland C, Laverdière J, Deslauriers M, Adams G, Fullarton M, Bohlmann J, Lenz P, Bousquet J. Genomic selection for resistance to spruce budworm in white spruce and relationships with growth and wood quality traits. Evol Appl 2020; 13:2704-2722. [PMID: 33294018 PMCID: PMC7691460 DOI: 10.1111/eva.13076] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 07/17/2020] [Accepted: 07/20/2020] [Indexed: 12/24/2022] Open
Abstract
With climate change, the pressure on tree breeding to provide varieties with improved resilience to biotic and abiotic stress is increasing. As such, pest resistance is of high priority but has been neglected in most tree breeding programs, given the complexity of phenotyping for these traits and delays to assess mature trees. In addition, the existing genetic variation of resistance and its relationship with productivity should be better understood for their consideration in multitrait breeding. In this study, we evaluated the prospects for genetic improvement of the levels of acetophenone aglycones (AAs) in white spruce needles, which have been shown to be tightly linked to resistance to spruce budworm. Furthermore, we estimated the accuracy of genomic selection (GS) for these traits, allowing selection at a very early stage to accelerate breeding. A total of 1,516 progeny trees established on five sites and belonging to 136 full-sib families from a mature breeding population in New Brunswick were measured for height growth and genotyped for 4,148 high-quality SNPs belonging to as many genes along the white spruce genome. In addition, 598 trees were assessed for levels of AAs piceol and pungenol in needles, and 578 for wood stiffness. GS models were developed with the phenotyped trees and then applied to predict the trait values of unphenotyped trees. AAs were under moderate-to-high genetic control (h 2: 0.43-0.57) with null or marginally negative genetic correlations with other traits. The prediction accuracy of GS models (GBLUP) for AAs was high (PAAC: 0.63-0.67) and comparable or slightly higher than pedigree-based (ABLUP) or BayesCπ models. We show that AA traits can be improved and that GS speeds up the selection of improved trees for insect resistance and for growth and wood quality traits. Various selection strategies were tested to optimize multitrait gains.
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Affiliation(s)
- Jean Beaulieu
- Canada Research Chair in Forest GenomicsInstitute of Systems and Integrative Biology and Systems, and Centre for Forest ResearchUniversité LavalQuébecQCCanada
| | - Simon Nadeau
- Natural Resources CanadaCanadian Wood Fibre CentreQuébecQCCanada
| | - Chen Ding
- Canada Research Chair in Forest GenomicsInstitute of Systems and Integrative Biology and Systems, and Centre for Forest ResearchUniversité LavalQuébecQCCanada
- Present address:
Western Gulf Forest Tree Improvement ProgramTexas A&M Forest ServiceForest Science LaboratoryCollege StationTXUSA
| | - Jose M. Celedon
- Michael Smith LaboratoriesUniversity of British ColumbiaVancouverBCCanada
| | - Aïda Azaiez
- Canada Research Chair in Forest GenomicsInstitute of Systems and Integrative Biology and Systems, and Centre for Forest ResearchUniversité LavalQuébecQCCanada
| | - Carol Ritland
- Michael Smith LaboratoriesUniversity of British ColumbiaVancouverBCCanada
- Department of Forest and Conservation SciencesUniversity of British ColumbiaVancouverBCCanada
| | - Jean‐Philippe Laverdière
- Canada Research Chair in Forest GenomicsInstitute of Systems and Integrative Biology and Systems, and Centre for Forest ResearchUniversité LavalQuébecQCCanada
| | | | | | - Michele Fullarton
- Forest Development SectionNatural Resources and Energy DevelopmentGovernment of New BrunswickIsland ViewNBCanada
| | - Joerg Bohlmann
- Michael Smith LaboratoriesUniversity of British ColumbiaVancouverBCCanada
- Department of Forest and Conservation SciencesUniversity of British ColumbiaVancouverBCCanada
- Department of BotanyUniversity of British ColumbiaVancouverBCCanada
| | - Patrick Lenz
- Canada Research Chair in Forest GenomicsInstitute of Systems and Integrative Biology and Systems, and Centre for Forest ResearchUniversité LavalQuébecQCCanada
- Natural Resources CanadaCanadian Wood Fibre CentreQuébecQCCanada
| | - Jean Bousquet
- Canada Research Chair in Forest GenomicsInstitute of Systems and Integrative Biology and Systems, and Centre for Forest ResearchUniversité LavalQuébecQCCanada
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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: 16] [Impact Index Per Article: 4.0] [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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13
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Genomic Selection for Forest Tree Improvement: Methods, Achievements and Perspectives. FORESTS 2020. [DOI: 10.3390/f11111190] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [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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14
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Cortés AJ, Restrepo-Montoya M, Bedoya-Canas LE. Modern Strategies to Assess and Breed Forest Tree Adaptation to Changing Climate. FRONTIERS IN PLANT SCIENCE 2020; 11:583323. [PMID: 33193532 PMCID: PMC7609427 DOI: 10.3389/fpls.2020.583323] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 09/29/2020] [Indexed: 05/02/2023]
Abstract
Studying the genetics of adaptation to new environments in ecologically and industrially important tree species is currently a major research line in the fields of plant science and genetic improvement for tolerance to abiotic stress. Specifically, exploring the genomic basis of local adaptation is imperative for assessing the conditions under which trees will successfully adapt in situ to global climate change. However, this knowledge has scarcely been used in conservation and forest tree improvement because woody perennials face major research limitations such as their outcrossing reproductive systems, long juvenile phase, and huge genome sizes. Therefore, in this review we discuss predictive genomic approaches that promise increasing adaptive selection accuracy and shortening generation intervals. They may also assist the detection of novel allelic variants from tree germplasm, and disclose the genomic potential of adaptation to different environments. For instance, natural populations of tree species invite using tools from the population genomics field to study the signatures of local adaptation. Conventional genetic markers and whole genome sequencing both help identifying genes and markers that diverge between local populations more than expected under neutrality, and that exhibit unique signatures of diversity indicative of "selective sweeps." Ultimately, these efforts inform the conservation and breeding status capable of pivoting forest health, ecosystem services, and sustainable production. Key long-term perspectives include understanding how trees' phylogeographic history may affect the adaptive relevant genetic variation available for adaptation to environmental change. Encouraging "big data" approaches (machine learning-ML) capable of comprehensively merging heterogeneous genomic and ecological datasets is becoming imperative, too.
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Affiliation(s)
- Andrés J. Cortés
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, Rionegro, Colombia
- Departamento de Ciencias Forestales, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia – Sede Medellín, Medellín, Colombia
| | - Manuela Restrepo-Montoya
- Departamento de Ciencias Forestales, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia – Sede Medellín, Medellín, Colombia
| | - Larry E. Bedoya-Canas
- Departamento de Ciencias Forestales, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia – Sede Medellín, Medellín, Colombia
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