1
|
Aguirre NC, Villalba PV, García MN, Filippi CV, Rivas JG, Martínez MC, Acuña CV, López AJ, López JA, Pathauer P, Palazzini D, Harrand L, Oberschelp J, Marcó MA, Cisneros EF, Carreras R, Martins Alves AM, Rodrigues JC, Hopp HE, Grattapaglia D, Cappa EP, Paniego NB, Marcucci Poltri SN. Comparison of ddRADseq and EUChip60K SNP genotyping systems for population genetics and genomic selection in Eucalyptus dunnii (Maiden). Front Genet 2024; 15:1361418. [PMID: 38606359 PMCID: PMC11008695 DOI: 10.3389/fgene.2024.1361418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 02/19/2024] [Indexed: 04/13/2024] Open
Abstract
Eucalyptus dunnii is one of the most important Eucalyptus species for short-fiber pulp production in regions where other species of the genus are affected by poor soil and climatic conditions. In this context, E. dunnii holds promise as a resource to address and adapt to the challenges of climate change. Despite its rapid growth and favorable wood properties for solid wood products, the advancement of its improvement remains in its early stages. In this work, we evaluated the performance of two single nucleotide polymorphism, (SNP), genotyping methods for population genetics analysis and Genomic Selection in E. dunnii. Double digest restriction-site associated DNA sequencing (ddRADseq) was compared with the EUChip60K array in 308 individuals from a provenance-progeny trial. The compared SNP set included 8,011 and 19,008 informative SNPs distributed along the 11 chromosomes, respectively. Although the two datasets differed in the percentage of missing data, genome coverage, minor allele frequency and estimated genetic diversity parameters, they revealed a similar genetic structure, showing two subpopulations with little differentiation between them, and low linkage disequilibrium. GS analyses were performed for eleven traits using Genomic Best Linear Unbiased Prediction (GBLUP) and a conventional pedigree-based model (ABLUP). Regardless of the SNP dataset, the predictive ability (PA) of GBLUP was better than that of ABLUP for six traits (Cellulose content, Total and Ethanolic extractives, Total and Klason lignin content and Syringyl and Guaiacyl lignin monomer ratio). When contrasting the SNP datasets used to estimate PAs, the GBLUP-EUChip60K model gave higher and significant PA values for six traits, meanwhile, the values estimated using ddRADseq gave higher values for three other traits. The PAs correlated positively with narrow sense heritabilities, with the highest correlations shown by the ABLUP and GBLUP-EUChip60K. The two genotyping methods, ddRADseq and EUChip60K, are generally comparable for population genetics and genomic prediction, demonstrating the utility of the former when subjected to rigorous SNP filtering. The results of this study provide a basis for future whole-genome studies using ddRADseq in non-model forest species for which SNP arrays have not yet been developed.
Collapse
Affiliation(s)
| | | | - Martín Nahuel García
- Instituto de Agrobiotecnología y Biología Molecular, UEDD INTA-CONICET, Hurlingham, Argentina
| | - Carla Valeria Filippi
- Instituto de Agrobiotecnología y Biología Molecular, UEDD INTA-CONICET, Hurlingham, Argentina
- Laboratorio de Bioquímica, Departamento de Biología Vegetal, Facultad de Agronomía, Universidad de la República, Montevideo, Uruguay
| | - Juan Gabriel Rivas
- Instituto de Agrobiotecnología y Biología Molecular, UEDD INTA-CONICET, Hurlingham, Argentina
| | - María Carolina Martínez
- Instituto de Agrobiotecnología y Biología Molecular, UEDD INTA-CONICET, Hurlingham, Argentina
| | - Cintia Vanesa Acuña
- Instituto de Agrobiotecnología y Biología Molecular, UEDD INTA-CONICET, Hurlingham, Argentina
| | - Augusto J. López
- Estación Experimental Agropecuaria de Bella Vista, Instituto Nacional de Tecnología Agropecuaria, Bella Vista, Argentina
| | - Juan Adolfo López
- Estación Experimental Agropecuaria de Bella Vista, Instituto Nacional de Tecnología Agropecuaria, Bella Vista, Argentina
| | - Pablo Pathauer
- Instituto de Recursos Biológicos, Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Argentina
| | - Dino Palazzini
- Instituto de Recursos Biológicos, Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Argentina
| | - Leonel Harrand
- Estación Experimental Agropecuaria de Concordia, Instituto Nacional de Tecnología Agropecuaria, Concordia, Argentina
| | - Javier Oberschelp
- Estación Experimental Agropecuaria de Concordia, Instituto Nacional de Tecnología Agropecuaria, Concordia, Argentina
| | - Martín Alberto Marcó
- Estación Experimental Agropecuaria de Concordia, Instituto Nacional de Tecnología Agropecuaria, Concordia, Argentina
| | - Esteban Felipe Cisneros
- Facultad de Ciencias Forestales, Universidad Nacional de Santiago del Estero (UNSE), Santiago del Estero, Argentina
| | - Rocío Carreras
- Facultad de Ciencias Forestales, Universidad Nacional de Santiago del Estero (UNSE), Santiago del Estero, Argentina
| | - Ana Maria Martins Alves
- Centro de Estudos Florestais e Laboratório Associado TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, Lisboa, Portugal
| | - José Carlos Rodrigues
- Centro de Estudos Florestais e Laboratório Associado TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, Lisboa, Portugal
| | - H. Esteban Hopp
- Instituto de Agrobiotecnología y Biología Molecular, UEDD INTA-CONICET, Hurlingham, Argentina
| | - Dario Grattapaglia
- Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA), Recursos Genéticos e Biotecnologia, Brasilia, Brazil
| | - Eduardo Pablo Cappa
- Instituto de Recursos Biológicos, Instituto Nacional de Tecnología Agropecuaria, Hurlingham, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina
| | - Norma Beatriz Paniego
- Instituto de Agrobiotecnología y Biología Molecular, UEDD INTA-CONICET, Hurlingham, Argentina
| | | |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Simiqueli GF, Resende RT, Takahashi EK, de Sousa JE, Grattapaglia D. Realized genomic selection across generations in a reciprocal recurrent selection breeding program of Eucalyptus hybrids. FRONTIERS IN PLANT SCIENCE 2023; 14:1252504. [PMID: 37965018 PMCID: PMC10641691 DOI: 10.3389/fpls.2023.1252504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 09/29/2023] [Indexed: 11/16/2023]
Abstract
Introduction Genomic selection (GS) experiments in forest trees have largely reported estimates of predictive abilities from cross-validation among individuals in the same breeding generation. In such conditions, no effects of recombination, selection, drift, and environmental changes are accounted for. Here, we assessed the effectively realized predictive ability (RPA) for volume growth at harvest age by GS across generations in an operational reciprocal recurrent selection (RRS) program of hybrid Eucalyptus. Methods Genomic best linear unbiased prediction with additive (GBLUP_G), additive plus dominance (GBLUP_G+D), and additive single-step (HBLUP) models were trained with different combinations of growth data of hybrids and pure species individuals (N = 17,462) of the G1 generation, 1,944 of which were genotyped with ~16,000 SNPs from SNP arrays. The hybrid G2 progeny trial (HPT267) was the GS target, with 1,400 selection candidates, 197 of which were genotyped still at the seedling stage, and genomically predicted for their breeding and genotypic values at the operational harvest age (6 years). Seedlings were then grown to harvest and measured, and their pedigree-based breeding and genotypic values were compared to their originally predicted genomic counterparts. Results Genomic RPAs ≥0.80 were obtained as the genetic relatedness between G1 and G2 increased, especially when the direct parents of selection candidates were used in training. GBLUP_G+D reached RPAs ≥0.70 only when hybrid or pure species data of G1 were included in training. HBLUP was only marginally better than GBLUP. Correlations ≥0.80 were obtained between pedigree and genomic individual ranks. Rank coincidence of the top 2.5% selections was the highest for GBLUP_G (45% to 60%) compared to GBLUP_G+D. To advance the pure species RRS populations, GS models were best when trained on pure species than hybrid data, and HBLUP yielded ~20% higher predictive abilities than GBLUP, but was not better than ABLUP for ungenotyped trees. Discussion We demonstrate that genomic data effectively enable accurate ranking of eucalypt hybrid seedlings for their yet-to-be observed volume growth at harvest age. Our results support a two-stage GS approach involving family selection by average genomic breeding value, followed by within-top-families individual GS, significantly increasing selection intensity, optimizing genotyping costs, and accelerating RRS breeding.
Collapse
Affiliation(s)
| | - Rafael Tassinari Resende
- School of Agronomy, Federal University of Goiás (UFG), Goiânia, GO, Brazil
- Department of Forestry, University of Brasília (UnB), Brasília, DF, Brazil
| | | | | | - Dario Grattapaglia
- Plant Genetics Laboratory, EMBRAPA Genetic Resources and Biotechnology, Brasilia, Brazil
| |
Collapse
|
5
|
Quezada M, Giorello FM, Da Silva CC, Aguilar I, Balmelli G. Single-step genome-wide association study for susceptibility to Teratosphaeria nubilosa and precocity of vegetative phase change in Eucalyptus globulus. FRONTIERS IN PLANT SCIENCE 2023; 14:1124768. [PMID: 37465383 PMCID: PMC10350686 DOI: 10.3389/fpls.2023.1124768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 05/24/2023] [Indexed: 07/20/2023]
Abstract
Introduction Mycosphaerella leaf disease (MLD) is one of the most prevalent foliar diseases of Eucalyptus globulus plantations around the world. Since resistance management strategies have not been effective in commercial plantations, breeding to develop more resistant genotypes is the most promising strategy. Available genomic information can be used to detect genomic regions associated with resistance to MLD, which could significantly speed up the process of genetic improvement. Methods We investigated the genetic basis of MLD resistance in a breeding population of E. globulus which was genotyped with the EUChip60K SNP array. Resistance to MLD was evaluated through resistance of the juvenile foliage, as defoliation and leaf spot severity, and through precocity of change to resistant adult foliage. Genome-wide association studies (GWAS) were carried out applying four Single-SNP models, a Genomic Best Linear Unbiased Prediction (GBLUP-GWAS) approach, and a Single-step genome-wide association study (ssGWAS). Results The Single-SNP (model K) and GBLUP-GWAS models detected 13 and 16 SNP-trait associations in chromosomes 2, 3 y 11; whereas the ssGWAS detected 66 SNP-trait associations in the same chromosomes, and additional significant SNP-trait associations in chromosomes 5 to 9 for the precocity of phase change (proportion of adult foliage). For this trait, the two main regions in chromosomes 3 and 11 were identified for the three approaches. The SNPs identified in these regions were positioned near the key miRNA genes, miR156.5 and miR157.4, which have a main role in the regulation of the timing of vegetative change, and also in the response to environmental stresses in plants. Discussion Our results demonstrated that ssGWAS was more powerful in detecting regions that affect resistance than conventional GWAS approaches. Additionally, the results suggest a polygenic genetic architecture for the heteroblastic transition in E. globulus and identified useful SNP markers for the development of marker-assisted selection strategies for resistance to MLD.
Collapse
Affiliation(s)
- Marianella Quezada
- Programa Nacional de Investigación en Producción de Leche, Estación Experimental “Wilson Ferreira Adulnate”, Instituto Nacional de Investigación Agropecuaria, Canelones, Uruguay
- Laboratorio de Biotecnología, Departamento de Biología Vegetal, Facultad de Agronomía, Universidad de la República, Montevideo, Uruguay
| | - Facundo Matias Giorello
- PDU Espacio de Biología Vegetal del Noreste, sede Tacuarembó, CENUR Noreste, Universidad de la República, Tacuarembó, Uruguay
| | - Cecilia Corina Da Silva
- PDU Espacio de Biología Vegetal del Noreste, sede Tacuarembó, CENUR Noreste, Universidad de la República, Tacuarembó, Uruguay
| | - Ignacio Aguilar
- Programa Nacional de Investigación en Producción de Leche, Estación Experimental “Wilson Ferreira Adulnate”, Instituto Nacional de Investigación Agropecuaria, Canelones, Uruguay
| | - Gustavo Balmelli
- Programa Nacional de Investigación en Producción Forestal, Estación Experimental del Norte, Instituto Nacional de Investigación Agropecuaria, Tacuarembó, Uruguay
| |
Collapse
|
6
|
Zhao W, Zhang Z, Ma P, Wang Z, Wang Q, Zhang Z, Pan Y. The effect of high-density genotypic data and different methods on joint genomic prediction: A case study in large white pigs. Anim Genet 2023; 54:45-54. [PMID: 36414135 DOI: 10.1111/age.13275] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 11/07/2022] [Accepted: 11/07/2022] [Indexed: 11/24/2022]
Abstract
Joint genomic prediction (GP) is an attractive method to improve the accuracy of GP by combining information from multiple populations. However, many factors can negatively influence the accuracy of joint GP, such as differences in linkage disequilibrium phasing between single nucleotide polymorphisms (SNPs) and causal variants, minor allele frequencies and causal variants' effect sizes across different populations. The objective of this study was to investigate whether the imputed high-density genotype data can improve the accuracy of joint GP using genomic best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP), multi-trait GBLUP (MT-GBLUP) and GBLUP based on genomic relationship matrix considering heterogenous minor allele frequencies across different populations (wGBLUP). Three traits, including days taken to reach slaughter weight, backfat thickness and loin muscle area, were measured on 67 276 Large White pigs from two different populations, for which 3334 were genotyped by SNP array. The results showed that a combined population could substantially improve the accuracy of GP compared with a single-population GP, especially for the population with a smaller size. The imputed SNP data had no effect for single population GP but helped to yield higher accuracy than the medium-density array data for joint GP. Of the four methods, ssGLBUP performed the best, but the advantage of ssGBLUP decreased as more individuals were genotyped. In some cases, MT-GBLUP and wGBLUP performed better than GBLUP. In conclusion, our results confirmed that joint GP could be beneficial from imputed high-density genotype data, and the wGBLUP and MT-GBLUP methods are promising for joint GP in pig breeding.
Collapse
Affiliation(s)
- Wei Zhao
- Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou, China
| | - Zhenyang Zhang
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Peipei Ma
- Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou, China
| | - Zhen Wang
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Qishan Wang
- Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou, China
| | - Zhe Zhang
- Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou, China
| | - Yuchun Pan
- Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou, China.,Hainan Research Institute, Zhejiang University, Sanya, China
| |
Collapse
|
7
|
Ding C, Weng Y, Byram TD, Bartlett BD, Raley EM. Post hoc experimental designs improve genetic trial analyses: A case study of cherrybark oak (Quercus pagoda Raf.) genetic evaluation in the western Gulf region, USA. PLoS One 2023; 18:e0285150. [PMID: 37172062 PMCID: PMC10180598 DOI: 10.1371/journal.pone.0285150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 04/18/2023] [Indexed: 05/14/2023] Open
Abstract
Oaks (Quercus spp.) are widespread hardwood trees in the Northern Hemisphere and of high ecological, economic, and social values. Optimal experimental design of genetic trials is essential for accurate estimates of genetic parameters and improving the genetic merit of breeding stock. Here, we evaluate the use of post hoc row-column factors combined with spatial adjustment to improve genetic analyses of parents and individual trees in field progeny tests of plantation hardwoods, using cherrybark oak (Quercus pagoda Raf.) as an example. For tree height, post hoc incomplete blocking reduced ~14% more of the within-block environmental variance compared to the randomized complete block design (RCBD) model. Incomplete blocking also improved the heritability estimates for height by 7% to 14% compared to the original RCBD model. No clinal trend for growth breeding values was identified due to provenances. Our approach warrants the initial selection for height as early as age ~10 based on its moderate narrow-sense heritability of 0.2; however, diameter and volume need longer evaluation times. The post hoc incomplete blocking is more efficient and promising to improve the genetic analysis of Q. pagoda to minimize the environmental heterogeneity influences. Adjusting competition and spatial effects, including the distance principal components and autoregressive residual structure notably improves the model fit based on the observed reductions in AICs and BICs. Employing our approach is promising for hardwood genetic improvement in the southern USA.
Collapse
Affiliation(s)
- Chen Ding
- Western Gulf Forest Tree Improvement Program, Texas A&M Forest Service, Texas A&M University System, College Station, Texas, United States of America
- College of Forestry, Wildlife and Environment, Auburn University, Auburn, Alabama, United States of America
| | - Yuhui Weng
- Arthur Temple College of Forestry and Agriculture, Stephen F. Austin State University, Nacogdoches, Texas, United States of America
| | - Tom D Byram
- Western Gulf Forest Tree Improvement Program, Texas A&M Forest Service, Texas A&M University System, College Station, Texas, United States of America
| | - Benjamin D Bartlett
- Western Gulf Forest Tree Improvement Program, Texas A&M Forest Service, Texas A&M University System, College Station, Texas, United States of America
| | - Earl M Raley
- Western Gulf Forest Tree Improvement Program, Texas A&M Forest Service, Texas A&M University System, College Station, Texas, United States of America
| |
Collapse
|
8
|
Nantongo JS, Potts BM, Klápště J, Graham NJ, Dungey HS, Fitzgerald H, O'Reilly-Wapstra JM. Genomic selection for resistance to mammalian bark stripping and associated chemical compounds in radiata pine. G3 (BETHESDA, MD.) 2022; 12:jkac245. [PMID: 36218439 PMCID: PMC9635650 DOI: 10.1093/g3journal/jkac245] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 08/29/2022] [Indexed: 07/28/2023]
Abstract
The integration of genomic data into genetic evaluations can facilitate the rapid selection of superior genotypes and accelerate the breeding cycle in trees. In this study, 390 trees from 74 control-pollinated families were genotyped using a 36K Axiom SNP array. A total of 15,624 high-quality SNPs were used to develop genomic prediction models for mammalian bark stripping, tree height, and selected primary and secondary chemical compounds in the bark. Genetic parameters from different genomic prediction methods-single-trait best linear unbiased prediction based on a marker-based relationship matrix (genomic best linear unbiased prediction), multitrait single-step genomic best linear unbiased prediction, which integrated the marker-based and pedigree-based relationship matrices (single-step genomic best linear unbiased prediction) and the single-trait generalized ridge regression-were compared to equivalent single- or multitrait pedigree-based approaches (ABLUP). The influence of the statistical distribution of data on the genetic parameters was assessed. Results indicated that the heritability estimates were increased nearly 2-fold with genomic models compared to the equivalent pedigree-based models. Predictive accuracy of the single-step genomic best linear unbiased prediction was higher than the ABLUP for most traits. Allowing for heterogeneity in marker effects through the use of generalized ridge regression did not markedly improve predictive ability over genomic best linear unbiased prediction, arguing that most of the chemical traits are modulated by many genes with small effects. Overall, the traits with low pedigree-based heritability benefited more from genomic models compared to the traits with high pedigree-based heritability. There was no evidence that data skewness or the presence of outliers affected the genomic or pedigree-based genetic estimates.
Collapse
Affiliation(s)
- Judith S Nantongo
- Corresponding author: National Agricultural Research Organization, P.O Box 1752, Mukono, Uganda.
| | - Brad M Potts
- School of Natural Sciences, University of Tasmania, Hobart, TAS 7001, Australia
- ARC Training Centre for Forest Value, Hobart, TAS 7001, Australia
| | - Jaroslav Klápště
- Scion (New Zealand Forest Research Institute Ltd.), Rotorua 3046, New Zealand
| | - Natalie J Graham
- Scion (New Zealand Forest Research Institute Ltd.), Rotorua 3046, New Zealand
| | - Heidi S Dungey
- Scion (New Zealand Forest Research Institute Ltd.), Rotorua 3046, New Zealand
| | - Hugh Fitzgerald
- School of Natural Sciences, University of Tasmania, Hobart, TAS 7001, Australia
| | - Julianne M O'Reilly-Wapstra
- School of Natural Sciences, University of Tasmania, Hobart, TAS 7001, Australia
- ARC Training Centre for Forest Value, Hobart, TAS 7001, Australia
| |
Collapse
|
9
|
Seyum EG, Bille NH, Abtew WG, Munyengwa N, Bell JM, Cros D. Genomic selection in tropical perennial crops and plantation trees: a review. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:58. [PMID: 37313015 PMCID: PMC10248687 DOI: 10.1007/s11032-022-01326-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 09/06/2022] [Indexed: 06/15/2023]
Abstract
To overcome the multiple challenges currently faced by agriculture, such as climate change and soil deterioration, more efficient plant breeding strategies are required. Genomic selection (GS) is crucial for the genetic improvement of quantitative traits, as it can increase selection intensity, shorten the generation interval, and improve selection accuracy for traits that are difficult to phenotype. Tropical perennial crops and plantation trees are of major economic importance and have consequently been the subject of many GS articles. In this review, we discuss the factors that affect GS accuracy (statistical models, linkage disequilibrium, information concerning markers, relatedness between training and target populations, the size of the training population, and trait heritability) and the genetic gain expected in these species. The impact of GS will be particularly strong in tropical perennial crops and plantation trees as they have long breeding cycles and constrained selection intensity. Future GS prospects are also discussed. High-throughput phenotyping will allow constructing of large training populations and implementing of phenomic selection. Optimized modeling is needed for longitudinal traits and multi-environment trials. The use of multi-omics, haploblocks, and structural variants will enable going beyond single-locus genotype data. Innovative statistical approaches, like artificial neural networks, are expected to efficiently handle the increasing amounts of heterogeneous multi-scale data. Targeted recombinations on sites identified from profiles of marker effects have the potential to further increase genetic gain. GS can also aid re-domestication and introgression breeding. Finally, GS consortia will play an important role in making the best of these opportunities. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01326-4.
Collapse
Affiliation(s)
- Essubalew Getachew Seyum
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
- Department of Horticulture and Plant Sciences, College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box 307, Jimma, Ethiopia
| | - Ngalle Hermine Bille
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
| | - Wosene Gebreselassie Abtew
- Department of Horticulture and Plant Sciences, College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box 307, Jimma, Ethiopia
| | - Norman Munyengwa
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Joseph Martin Bell
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
| | - David Cros
- CIRAD, UMR AGAP Institut, 34398 Montpellier, France
- UMR AGAP Institut, CIRAD, INRAE, Univ. Montpellier, Institut Agro, 34398 Montpellier, France
| |
Collapse
|
10
|
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.
Collapse
|
11
|
Thumma BR, Joyce KR, Jacobs A. Genomic studies with preselected markers reveal dominance effects influencing growth traits in Eucalyptus nitens. G3 GENES|GENOMES|GENETICS 2022; 12:6423988. [PMID: 34791210 PMCID: PMC8728041 DOI: 10.1093/g3journal/jkab363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 10/13/2021] [Indexed: 11/17/2022]
Abstract
Genomic selection (GS) is being increasingly adopted by the tree breeding community. Most of the GS studies in trees are focused on estimating additive genetic effects. Exploiting the dominance effects offers additional opportunities to improve genetic gain. To detect dominance effects, trait-relevant markers may be important compared to nonselected markers. Here, we used preselected markers to study the dominance effects in a Eucalyptus nitens (E. nitens) breeding population consisting of open-pollinated (OP) and controlled-pollinated (CP) families. We used 8221 trees from six progeny trials in this study. Of these, 868 progeny and 255 parents were genotyped with the E. nitens marker panel. Three traits; diameter at breast height (DBH), wood basic density (DEN), and kraft pulp yield (KPY) were analyzed. Two types of genomic relationship matrices based on identity-by-state (IBS) and identity-by-descent (IBD) were tested. Performance of the genomic best linear unbiased prediction (GBLUP) models with IBS and IBD matrices were compared with pedigree-based additive best linear unbiased prediction (ABLUP) models with and without the pedigree reconstruction. Similarly, the performance of the single-step GBLUP (ssGBLUP) with IBS and IBD matrices were compared with ABLUP models using all 8221 trees. Significant dominance effects were observed with the GBLUP-AD model for DBH. The predictive ability of DBH is higher with the GBLUP-AD model compared to other models. Similarly, the prediction accuracy of genotypic values is higher with GBLUP-AD compared to the GBLUP-A model. Among the two GBLUP models (IBS and IBD), no differences were observed in predictive abilities and prediction accuracies. While the estimates of predictive ability with additive effects were similar among all four models, prediction accuracies of ABLUP were lower than the GBLUP models. The prediction accuracy of ssGBLUP-IBD is higher than the other three models while the theoretical accuracy of ssGBLUP-IBS is consistently higher than the other three models across all three groups tested (parents, genotyped, and nongenotyped). Significant inbreeding depression was observed for DBH and KPY. While there is a linear relationship between inbreeding and DBH, the relationship between inbreeding and KPY is nonlinear and quadratic. These results indicate that the inbreeding depression of DBH is mainly due to directional dominance while in KPY it may be due to epistasis. Inbreeding depression may be the main source of the observed dominance effects in DBH. The significant dominance effect observed for DBH may be used to select complementary parents to improve the genetic merit of the progeny in E. nitens.
Collapse
Affiliation(s)
- Bala R Thumma
- Gondwana Genomics Pty Ltd , Canberra, ACT 2600, Australia
| | | | | |
Collapse
|
12
|
Cañas-Gutiérrez GP, Sepulveda-Ortega S, López-Hernández F, Navas-Arboleda AA, Cortés AJ. Inheritance of Yield Components and Morphological Traits in Avocado cv. Hass From "Criollo" "Elite Trees" via Half-Sib Seedling Rootstocks. FRONTIERS IN PLANT SCIENCE 2022; 13:843099. [PMID: 35685008 PMCID: PMC9171141 DOI: 10.3389/fpls.2022.843099] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 02/10/2022] [Indexed: 05/11/2023]
Abstract
Grafting induces precocity and maintains clonal integrity in fruit tree crops. However, the complex rootstock × scion interaction often precludes understanding how the tree phenotype is shaped, limiting the potential to select optimum rootstocks. Therefore, it is necessary to assess (1) how seedling progenies inherit trait variation from elite 'plus trees', and (2) whether such family superiority may be transferred after grafting to the clonal scion. To bridge this gap, we quantified additive genetic parameters (i.e., narrow sense heritability-h 2, and genetic-estimated breeding values-GEBVs) across landraces, "criollo", "plus trees" of the super-food fruit tree crop avocado (Persea americana Mill.), and their open-pollinated (OP) half-sib seedling families. Specifically, we used a genomic best linear unbiased prediction (G-BLUP) model to merge phenotypic characterization of 17 morpho-agronomic traits with genetic screening of 13 highly polymorphic SSR markers in a diverse panel of 104 avocado "criollo" "plus trees." Estimated additive genetic parameters were validated at a 5-year-old common garden trial (i.e., provenance test), in which 22 OP half-sib seedlings from 82 elite "plus trees" served as rootstocks for the cv. Hass clone. Heritability (h 2) scores in the "criollo" "plus trees" ranged from 0.28 to 0.51. The highest h 2 values were observed for ribbed petiole and adaxial veins with 0.47 (CI 95%0.2-0.8) and 0.51 (CI 0.2-0.8), respectively. The h 2 scores for the agronomic traits ranged from 0.34 (CI 0.2-0.6) to 0.39 (CI 0.2-0.6) for seed weight, fruit weight, and total volume, respectively. When inspecting yield variation across 5-year-old grafted avocado cv. Hass trees with elite OP half-sib seedling rootstocks, the traits total number of fruits and fruits' weight, respectively, exhibited h 2 scores of 0.36 (± 0.23) and 0.11 (± 0.09). Our results indicate that elite "criollo" "plus trees" may serve as promissory donors of seedling rootstocks for avocado cv. Hass orchards due to the inheritance of their outstanding trait values. This reinforces the feasibility to leverage natural variation from "plus trees" via OP half-sib seedling rootstock families. By jointly estimating half-sib family effects and rootstock-mediated heritability, this study promises boosting seedling rootstock breeding programs, while better discerning the consequences of grafting in fruit tree crops.
Collapse
Affiliation(s)
- Gloria Patricia Cañas-Gutiérrez
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, Colombia
- Corporation for Biological Research (CIB), Unit of Phytosanity and Biological Control, Medellín, Colombia
- *Correspondence: Gloria Patricia Cañas-Gutiérrez,
| | - Stella Sepulveda-Ortega
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, Colombia
| | - Felipe López-Hernández
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, Colombia
| | | | - Andrés J. Cortés
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Rionegro, Colombia
- Andrés J. Cortés,
| |
Collapse
|
13
|
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.
Collapse
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;
| |
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
de Vries L, Guevara-Rozo S, Cho M, Liu LY, Renneckar S, Mansfield SD. Tailoring renewable materials via plant biotechnology. BIOTECHNOLOGY FOR BIOFUELS 2021; 14:167. [PMID: 34353358 PMCID: PMC8344217 DOI: 10.1186/s13068-021-02010-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 07/06/2021] [Indexed: 05/03/2023]
Abstract
Plants inherently display a rich diversity in cell wall chemistry, as they synthesize an array of polysaccharides along with lignin, a polyphenolic that can vary dramatically in subunit composition and interunit linkage complexity. These same cell wall chemical constituents play essential roles in our society, having been isolated by a variety of evolving industrial processes and employed in the production of an array of commodity products to which humans are reliant. However, these polymers are inherently synthesized and intricately packaged into complex structures that facilitate plant survival and adaptation to local biogeoclimatic regions and stresses, not for ease of deconstruction and commercial product development. Herein, we describe evolving techniques and strategies for altering the metabolic pathways related to plant cell wall biosynthesis, and highlight the resulting impact on chemistry, architecture, and polymer interactions. Furthermore, this review illustrates how these unique targeted cell wall modifications could significantly extend the number, diversity, and value of products generated in existing and emerging biorefineries. These modifications can further target the ability for processing of engineered wood into advanced high performance materials. In doing so, we attempt to illuminate the complex connection on how polymer chemistry and structure can be tailored to advance renewable material applications, using all the chemical constituents of plant-derived biopolymers, including pectins, hemicelluloses, cellulose, and lignins.
Collapse
Affiliation(s)
- Lisanne de Vries
- Department of Wood Science, Faculty of Forestry, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
- US Department of Energy (DOE) Great Lakes Bioenergy Research Center, the Wisconsin Energy Institute, University of Wisconsin - Madison, Madison, WI , 53726, USA
| | - Sydne Guevara-Rozo
- Department of Wood Science, Faculty of Forestry, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - MiJung Cho
- Department of Wood Science, Faculty of Forestry, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Li-Yang Liu
- Department of Wood Science, Faculty of Forestry, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Scott Renneckar
- Department of Wood Science, Faculty of Forestry, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Shawn D Mansfield
- Department of Wood Science, Faculty of Forestry, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.
- US Department of Energy (DOE) Great Lakes Bioenergy Research Center, the Wisconsin Energy Institute, University of Wisconsin - Madison, Madison, WI , 53726, USA.
| |
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|
17
|
Mancin E, Lourenco D, Bermann M, Mantovani R, Misztal I. Accounting for Population Structure and Phenotypes From Relatives in Association Mapping for Farm Animals: A Simulation Study. Front Genet 2021; 12:642065. [PMID: 33995481 PMCID: PMC8117227 DOI: 10.3389/fgene.2021.642065] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 04/07/2021] [Indexed: 11/16/2022] Open
Abstract
Population structure or genetic relatedness should be considered in genome association studies to avoid spurious association. The most used methods for genome-wide association studies (GWAS) account for population structure but are limited to genotyped individuals with phenotypes. Single-step GWAS (ssGWAS) can use phenotypes from non-genotyped relatives; however, its ability to account for population structure has not been explored. Here we investigate the equivalence among ssGWAS, efficient mixed-model association expedited (EMMAX), and genomic best linear unbiased prediction GWAS (GBLUP-GWAS), and how they differ from the single-SNP analysis without correction for population structure (SSA-NoCor). We used simulated, structured populations that mimicked fish, beef cattle, and dairy cattle populations with 1040, 5525, and 1,400 genotyped individuals, respectively. Larger populations were also simulated that had up to 10-fold more genotyped animals. The genomes were composed by 29 chromosomes, each harboring one QTN, and the number of simulated SNPs was 35,000 for the fish and 65,000 for the beef and dairy cattle populations. Males and females were genotyped in the fish and beef cattle populations, whereas only males had genotypes in the dairy population. Phenotypes for a trait with heritability varying from 0.25 to 0.35 were available in both sexes for the fish population, but only for females in the beef and dairy cattle populations. In the latter, phenotypes of daughters were projected into genotyped sires (i.e., deregressed proofs) before applying EMMAX and SSA-NoCor. Although SSA-NoCor had the largest number of true positive SNPs among the four methods, the number of false negatives was two–fivefold that of true positives. GBLUP-GWAS and EMMAX had a similar number of true positives, which was slightly smaller than in ssGWAS, although the difference was not significant. Additionally, no significant differences were observed when deregressed proofs were used as pseudo-phenotypes in EMMAX compared to daughter phenotypes in ssGWAS for the dairy cattle population. Single-step GWAS accounts for population structure and is a straightforward method for association analysis when only a fraction of the population is genotyped and/or when phenotypes are available on non-genotyped relatives.
Collapse
Affiliation(s)
- Enrico Mancin
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Padua, Italy.,Department of Animal and Dairy Science, University of Georgia, Athens, GA, United States
| | - Daniela Lourenco
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, United States
| | - Matias Bermann
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, United States
| | - Roberto Mantovani
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Padua, Italy
| | - Ignacy Misztal
- Department of Animal and Dairy Science, University of Georgia, Athens, GA, United States
| |
Collapse
|
18
|
Mphahlele MM, Isik F, Hodge GR, Myburg AA. Genomic Breeding for Diameter Growth and Tolerance to Leptocybe Gall Wasp and Botryosphaeria/ Teratosphaeria Fungal Disease Complex in Eucalyptus grandis. FRONTIERS IN PLANT SCIENCE 2021; 12:638969. [PMID: 33719317 PMCID: PMC7952757 DOI: 10.3389/fpls.2021.638969] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 01/29/2021] [Indexed: 05/16/2023]
Abstract
Eucalyptus grandis is one of the most important species for hardwood plantation forestry around the world. At present, its commercial deployment is in decline because of pests and pathogens such as Leptocybe invasa gall wasp (Lepto), and often co-occurring fungal stem diseases such as Botryosphaeria dothidea and Teratosphaeria zuluensis (BotryoTera). This study analyzed Lepto, BotryoTera, and stem diameter growth in an E. grandis multi-environmental, genetic trial. The study was established in three subtropical environments. Diameter growth and BotryoTera incidence scores were assessed on 3,334 trees, and Lepto incidence was assessed on 4,463 trees from 95 half-sib families. Using the Eucalyptus EUChip60K SNP chip, a subset of 964 trees from 93 half-sib families were genotyped with 14,347 informative SNP markers. We employed single-step genomic BLUP (ssGBLUP) to estimate genetic parameters in the genetic trial. Diameter and Lepto tolerance showed a positive genetic correlation (0.78), while BotryoTera tolerance had a negative genetic correlation with diameter growth (-0.38). The expected genetic gains for diameter growth and Lepto and BotryoTera tolerance were 12.4, 10, and -3.4%, respectively. We propose a genomic selection breeding strategy for E. grandis that addresses some of the present population structure problems.
Collapse
Affiliation(s)
- Makobatjatji M. Mphahlele
- Mondi Forests, Research and Development Department, Trahar Technology Centre – TTC, Hilton, South Africa
- Department of Biochemistry, Genetics and Microbiology, Forestry and Agricultural Biotechnology Institute, University of Pretoria, Pretoria, South Africa
| | - Fikret Isik
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, United States
| | - Gary R. Hodge
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, United States
- Camcore, North Carolina State University, Raleigh, NC, United States
| | - Alexander A. Myburg
- Department of Biochemistry, Genetics and Microbiology, Forestry and Agricultural Biotechnology Institute, University of Pretoria, Pretoria, South Africa
- *Correspondence: Alexander A. Myburg,
| |
Collapse
|
19
|
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.
Collapse
|
20
|
Pégard M, Segura V, Muñoz F, Bastien C, Jorge V, Sanchez L. Favorable Conditions for Genomic Evaluation to Outperform Classical Pedigree Evaluation Highlighted by a Proof-of-Concept Study in Poplar. FRONTIERS IN PLANT SCIENCE 2020; 11:581954. [PMID: 33193528 PMCID: PMC7655903 DOI: 10.3389/fpls.2020.581954] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 09/22/2020] [Indexed: 06/11/2023]
Abstract
Forest trees like poplar are particular in many ways compared to other domesticated species. They have long juvenile phases, ongoing crop-wild gene flow, extensive outcrossing, and slow growth. All these particularities tend to make the conduction of breeding programs and evaluation stages costly both in time and resources. Perennials like trees are therefore good candidates for the implementation of genomic selection (GS) which is a good way to accelerate the breeding process, by unchaining selection from phenotypic evaluation without affecting precision. In this study, we tried to compare GS to pedigree-based traditional evaluation, and evaluated under which conditions genomic evaluation outperforms classical pedigree evaluation. Several conditions were evaluated as the constitution of the training population by cross-validation, the implementation of multi-trait, single trait, additive and non-additive models with different estimation methods (G-BLUP or weighted G-BLUP). Finally, the impact of the marker densification was tested through four marker density sets. The population under study corresponds to a pedigree of 24 parents and 1,011 offspring, structured into 35 full-sib families. Four evaluation batches were planted in the same location and seven traits were evaluated on 1 and 2 years old trees. The quality of prediction was reported by the accuracy, the Spearman rank correlation and prediction bias and tested with a cross-validation and an independent individual test set. Our results show that genomic evaluation performance could be comparable to the already well-optimized pedigree-based evaluation under certain conditions. Genomic evaluation appeared to be advantageous when using an independent test set and a set of less precise phenotypes. Genome-based methods showed advantages over pedigree counterparts when ranking candidates at the within-family levels, for most of the families. Our study also showed that looking at ranking criteria as Spearman rank correlation can reveal benefits to genomic selection hidden by biased predictions.
Collapse
Affiliation(s)
| | - Vincent Segura
- BioForA, INRA, ONF, Orléans, France
- AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | | | | | | | | |
Collapse
|
21
|
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.
Collapse
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
| |
Collapse
|
22
|
Genomic Studies Reveal Substantial Dominant Effects and Improved Genomic Predictions in an Open-Pollinated Breeding Population of Eucalyptus pellita. G3-GENES GENOMES GENETICS 2020; 10:3751-3763. [PMID: 32788286 PMCID: PMC7534421 DOI: 10.1534/g3.120.401601] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Most of the genomic studies in plants and animals have used additive models for studying genetic parameters and prediction accuracies. In this study, we used genomic models with additive and nonadditive effects to analyze the genetic architecture of growth and wood traits in an open-pollinated (OP) population of Eucalyptus pellita. We used two progeny trials consisting of 5742 trees from 244 OP families to estimate genetic parameters and to test genomic prediction accuracies of three growth traits (diameter at breast height - DBH, total height - Ht and tree volume - Vol) and kraft pulp yield (KPY). From 5742 trees, 468 trees from 28 families were genotyped with 2023 pre-selected markers from candidate genes. We used the pedigree-based additive best linear unbiased prediction (ABLUP) model and two marker-based models (single-step genomic BLUP – ssGBLUP and genomic BLUP – GBLUP) to estimate the genetic parameters and compare the prediction accuracies. Analyses with the two genomic models revealed large dominant effects influencing the growth traits but not KPY. Theoretical breeding value accuracies were higher with the dominance effect in ssGBLUP model for the three growth traits. Accuracies of cross-validation with random folding in the genotyped trees have ranged from 0.60 to 0.82 in different models. Accuracies of ABLUP were lower than the genomic models. Accuracies ranging from 0.50 to 0.76 were observed for within family cross-validation predictions with low relationships between training and validation populations indicating part of the functional variation is captured by the markers through short-range linkage disequilibrium (LD). Within-family phenotype predictive abilities and prediction accuracies of genetic values with dominance effects are higher than the additive models for growth traits indicating the importance of dominance effects in predicting phenotypes and genetic values. This study demonstrates the importance of genomic approaches in OP families to study nonadditive effects. To capture the LD between markers and the quantitative trait loci (QTL) it may be important to use informative markers from candidate genes.
Collapse
|
23
|
Moreira FF, Oliveira HR, Volenec JJ, Rainey KM, Brito LF. Integrating High-Throughput Phenotyping and Statistical Genomic Methods to Genetically Improve Longitudinal Traits in Crops. FRONTIERS IN PLANT SCIENCE 2020; 11:681. [PMID: 32528513 PMCID: PMC7264266 DOI: 10.3389/fpls.2020.00681] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 04/30/2020] [Indexed: 05/28/2023]
Abstract
The rapid development of remote sensing in agronomic research allows the dynamic nature of longitudinal traits to be adequately described, which may enhance the genetic improvement of crop efficiency. For traits such as light interception, biomass accumulation, and responses to stressors, the data generated by the various high-throughput phenotyping (HTP) methods requires adequate statistical techniques to evaluate phenotypic records throughout time. As a consequence, information about plant functioning and activation of genes, as well as the interaction of gene networks at different stages of plant development and in response to environmental stimulus can be exploited. In this review, we outline the current analytical approaches in quantitative genetics that are applied to longitudinal traits in crops throughout development, describe the advantages and pitfalls of each approach, and indicate future research directions and opportunities.
Collapse
Affiliation(s)
- Fabiana F. Moreira
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Hinayah R. Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Jeffrey J. Volenec
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Katy M. Rainey
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| |
Collapse
|
24
|
Optimizing ddRADseq in Non-Model Species: A Case Study in Eucalyptus dunnii Maiden. AGRONOMY-BASEL 2019. [DOI: 10.3390/agronomy9090484] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Restriction site-associated DNA sequencing (RADseq) and its derived protocols, such as double digest RADseq (ddRADseq), offer a flexible and highly cost-effective strategy for efficient plant genome sampling. This has become one of the most popular genotyping approaches for breeding, conservation, and evolution studies in model and non-model plant species. However, universal protocols do not always adapt well to non-model species. Herein, this study reports the development of an optimized and detailed ddRADseq protocol in Eucalyptus dunnii, a non-model species, which combines different aspects of published methodologies. The initial protocol was established using only two samples by selecting the best combination of enzymes and through optimal size selection and simplifying lab procedures. Both single nucleotide polymorphisms (SNPs) and simple sequence repeats (SSRs) were determined with high accuracy after applying stringent bioinformatics settings and quality filters, with and without a reference genome. To scale it up to 24 samples, we added barcoded adapters. We also applied automatic size selection, and therefore obtained an optimal number of loci, the expected SNP locus density, and genome-wide distribution. Reliability and cross-sequencing platform compatibility were verified through dissimilarity coefficients of 0.05 between replicates. To our knowledge, this optimized ddRADseq protocol will allow users to go from the DNA sample to genotyping data in a highly accessible and reproducible way.
Collapse
|