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Wang H, Bai Y, Biligetu B. Effects of SNP marker density and training population size on prediction accuracy in alfalfa (Medicago sativa L.) genomic selection. THE PLANT GENOME 2024; 17:e20431. [PMID: 38263612 DOI: 10.1002/tpg2.20431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 11/29/2023] [Accepted: 01/04/2024] [Indexed: 01/25/2024]
Abstract
Effects of individual single-nucleotide polymorphism (SNP) markers and the size of "training" and "test" populations affect prediction accuracy in genomic selection (GS). This study evaluated 11 subsets of 4932 SNPs using six genetic additive methods to understand marker density in GS prediction in alfalfa (Medicago sativa L.). In the GS methods, the effect of "training" to "test" population size was also evaluated. Fourteen alfalfa populations sampled from long-term grazing sites were genotyped using genotyping by sequencing for the identification of SNPs. These populations were also phenotyped for six agromorphological and three nutritive traits from 2018 to 2020. The accuracy of GS prediction improved across six GS methods when the ratio of "training" to "test" population size increased. However, the prediction accuracy of the six GS methods reduced to a range of -0.27 to 0.11 when random, uninformative SNPs were used. In this study, five Bayesian methods and ridge-regression best linear unbiased prediction (rrBLUP) method had similar GS accuracies for "training" sets, but rrBLUP tended to outperform Bayesian methods in independent "test" sets when SNP subsets with high mean-squared-estimated-marker effect were used. These findings can enhance the application of GS in alfalfa genetic improvement.
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Affiliation(s)
- Hu Wang
- Department of Plant Sciences, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Yuguang Bai
- Department of Plant Sciences, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Bill Biligetu
- Department of Plant Sciences, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
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2
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Dong L, Xie Y, Zhang Y, Wang R, Sun X. Genomic dissection of additive and non-additive genetic effects and genomic prediction in an open-pollinated family test of Japanese larch. BMC Genomics 2024; 25:11. [PMID: 38166605 PMCID: PMC10759612 DOI: 10.1186/s12864-023-09891-4] [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: 06/14/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024] Open
Abstract
Genomic dissection of genetic effects on desirable traits and the subsequent use of genomic selection hold great promise for accelerating the rate of genetic improvement of forest tree species. In this study, a total of 661 offspring trees from 66 open-pollinated families of Japanese larch (Larix kaempferi (Lam.) Carrière) were sampled at a test site. The contributions of additive and non-additive effects (dominance, imprinting and epistasis) were evaluated for nine valuable traits related to growth, wood physical and chemical properties, and competitive ability using three pedigree-based and four Genomics-based Best Linear Unbiased Predictions (GBLUP) models and used to determine the genetic model. The predictive ability (PA) of two genomic prediction methods, GBLUP and Reproducing Kernel Hilbert Spaces (RKHS), was compared. The traits could be classified into two types based on different quantitative genetic architectures: for type I, including wood chemical properties and Pilodyn penetration, additive effect is the main source of variation (38.20-67.46%); for type II, including growth, competitive ability and acoustic velocity, epistasis plays a significant role (50.76-91.26%). Dominance and imprinting showed low to moderate contributions (< 36.26%). GBLUP was more suitable for traits of type I (PAs = 0.37-0.39 vs. 0.14-0.25), and RKHS was more suitable for traits of type II (PAs = 0.23-0.37 vs. 0.07-0.23). Non-additive effects make no meaningful contribution to the enhancement of PA of GBLUP method for all traits. These findings enhance our current understanding of the architecture of quantitative traits and lay the foundation for the development of genomic selection strategies in Japanese larch.
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Affiliation(s)
- Leiming Dong
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China
- Key Laboratory of National Forestry and Grassland Administration on Plant Ex situ Conservation, Beijing Floriculture Engineering Technology Research Centre, Beijing Botanical Garden, Beijing, 100093, China
| | - Yunhui Xie
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China
| | - Yalin Zhang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China
| | - Ruizhen Wang
- Key Laboratory of National Forestry and Grassland Administration on Plant Ex situ Conservation, Beijing Floriculture Engineering Technology Research Centre, Beijing Botanical Garden, Beijing, 100093, China
| | - Xiaomei Sun
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China.
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Shu M, Moran EV. Identifying genetic variation associated with environmental gradients and drought-tolerance phenotypes in ponderosa pine. Ecol Evol 2023; 13:e10620. [PMID: 37841219 PMCID: PMC10576020 DOI: 10.1002/ece3.10620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 09/05/2023] [Accepted: 10/04/2023] [Indexed: 10/17/2023] Open
Abstract
As climate changes, understanding the genetic basis of local adaptation in plants becomes an ever more pressing issue. Combining genotype-environment association (GEA) with genotype-phenotype association (GPA) analysis has an exciting potential to uncover the genetic basis of environmental responses. We use these approaches to identify genetic variants linked to local adaptation to drought in Pinus ponderosa. Over 4 million Single Nucleotide Polymorphisms (SNPs) were identified using 223 individuals from across the Sierra Nevada of California. 927,740 (22.3%) SNPs were retained after filtering for proximity to genes and used in our association analyses. We found 1374 associated with five major climate variables, with the largest number (1151) associated with April 1st snowpack. We also conducted a greenhouse study with various drought-tolerance traits measured in first-year seedlings of a subset of the genotyped trees grown in the greenhouse. 796 SNPs were associated with control-condition trait values, while 1149 were associated with responsiveness of these traits to drought. While no individual SNPs were associated with both the environmental variables and the measured traits, several annotated genes were associated with both, particularly those involved in cell wall formation, biotic and abiotic stress responses, and ubiquitination. However, the functions of many of the associated genes have not yet been determined due to the lack of gene annotation information for conifers. Future studies are needed to assess the developmental roles and ecological significance of these unknown genes.
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Affiliation(s)
- Mengjun Shu
- Life and Environmental SciencesUniversity of CaliforniaMercedCaliforniaUSA
| | - Emily V. Moran
- Life and Environmental SciencesUniversity of CaliforniaMercedCaliforniaUSA
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4
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Nadeau S, Beaulieu J, Gezan SA, Perron M, Bousquet J, Lenz PRN. Increasing genomic prediction accuracy for unphenotyped full-sib families by modeling additive and dominance effects with large datasets in white spruce. FRONTIERS IN PLANT SCIENCE 2023; 14:1137834. [PMID: 37035077 PMCID: PMC10073444 DOI: 10.3389/fpls.2023.1137834] [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: 01/04/2023] [Accepted: 02/14/2023] [Indexed: 06/19/2023]
Abstract
INTRODUCTION Genomic selection is becoming a standard technique in plant breeding and is now being introduced into forest tree breeding. Despite promising results to predict the genetic merit of superior material based on their additive breeding values, many studies and operational programs still neglect non-additive effects and their potential for enhancing genetic gains. METHODS Using two large comprehensive datasets totaling 4,066 trees from 146 full-sib families of white spruce (Picea glauca (Moench) Voss), we evaluated the effect of the inclusion of dominance on the precision of genetic parameter estimates and on the accuracy of conventional pedigree-based (ABLUP-AD) and genomic-based (GBLUP-AD) models. RESULTS While wood quality traits were mostly additively inherited, considerable non-additive effects and lower heritabilities were detected for growth traits. For growth, GBLUP-AD better partitioned the additive and dominance effects into roughly equal variances, while ABLUP-AD strongly overestimated dominance. The predictive abilities of breeding and total genetic value estimates were similar between ABLUP-AD and GBLUP-AD when predicting individuals from the same families as those included in the training dataset. However, GBLUP-AD outperformed ABLUP-AD when predicting for new unphenotyped families that were not represented in the training dataset, with, on average, 22% and 53% higher predictive ability of breeding and genetic values, respectively. Resampling simulations showed that GBLUP-AD required smaller sample sizes than ABLUP-AD to produce precise estimates of genetic variances and accurate predictions of genetic values. Still, regardless of the method used, large training datasets were needed to estimate additive and non-additive genetic variances precisely. DISCUSSION This study highlights the different quantitative genetic architectures between growth and wood traits. Furthermore, the usefulness of genomic additive-dominance models for predicting new families should allow practicing mating allocation to maximize the total genetic values for the propagation of elite material.
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Affiliation(s)
- Simon Nadeau
- Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, Québec, QC, Canada
| | - Jean Beaulieu
- Canada Research Chair in Forest Genomics, Institute for Systems and Integrative Biology, Université Laval, Québec, QC, Canada
| | | | - Martin Perron
- Canada Research Chair in Forest Genomics, Institute for Systems and Integrative Biology, Université Laval, Québec, QC, Canada
- Direction de la Recherche Forestière, Ministère des Ressources Naturelles et des Forêts, Québec, QC, Canada
| | - Jean Bousquet
- Canada Research Chair in Forest Genomics, Institute for Systems and Integrative Biology, Université Laval, Québec, QC, Canada
| | - Patrick R. N. Lenz
- Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, Québec, QC, Canada
- Canada Research Chair in Forest Genomics, Institute for Systems and Integrative Biology, Université Laval, Québec, QC, Canada
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Hu X, Carver BF, El-Kassaby YA, Zhu L, Chen C. Weighted kernels improve multi-environment genomic prediction. Heredity (Edinb) 2023; 130:82-91. [PMID: 36522412 PMCID: PMC9905581 DOI: 10.1038/s41437-022-00582-6] [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: 08/25/2021] [Revised: 11/27/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022] Open
Abstract
Crucial to variety improvement programs is the reliable and accurate prediction of genotype's performance across environments. However, due to the impactful presence of genotype by environment (G×E) interaction that dictates how changes in expression and function of genes influence target traits in different environments, prediction performance of genomic selection (GS) using single-environment models often falls short. Furthermore, despite the successes of genome-wide association studies (GWAS), the genetic insights derived from genome-to-phenome mapping have not yet been incorporated in predictive analytics, making GS models that use Gaussian kernel primarily an estimator of genomic similarity, instead of the underlying genetics characteristics of the populations. Here, we developed a GS framework that, in addition to capturing the overall genomic relationship, can capitalize on the signal of genetic associations of the phenotypic variation as well as the genetic characteristics of the populations. The capacity of predicting the performance of populations across environments was demonstrated by an overall gain in predictability up to 31% for the winter wheat DH population. Compared to Gaussian kernels, we showed that our multi-environment weighted kernels could better leverage the significance of genetic associations and yielded a marked improvement of 4-33% in prediction accuracy for half-sib families. Furthermore, the flexibility incorporated in our Bayesian implementation provides the generalizable capacity required for predicting multiple highly genetic heterogeneous populations across environments, allowing reliable GS for genetic improvement programs that have no access to genetically uniform material.
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Affiliation(s)
- Xiaowei Hu
- grid.65519.3e0000 0001 0721 7331Department of Statistics, Oklahoma State University, Stillwater, OK USA ,grid.27755.320000 0000 9136 933XPresent Address: Center for Public Health Genomics, University of Virginia, Charlottesville, VA USA
| | - Brett F. Carver
- grid.65519.3e0000 0001 0721 7331Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK USA
| | - Yousry A. El-Kassaby
- grid.17091.3e0000 0001 2288 9830Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, BC Canada
| | - Lan Zhu
- grid.65519.3e0000 0001 0721 7331Department of Statistics, Oklahoma State University, Stillwater, OK USA
| | - Charles Chen
- Department of Biochemistry and Molecular Biology, Oklahoma State University, Stillwater, OK, USA.
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Younessi-Hamzekhanlu M, Gailing O. Genome-Wide SNP Markers Accelerate Perennial Forest Tree Breeding Rate for Disease Resistance through Marker-Assisted and Genome-Wide Selection. Int J Mol Sci 2022; 23:ijms232012315. [PMID: 36293169 PMCID: PMC9604372 DOI: 10.3390/ijms232012315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/05/2022] [Accepted: 10/07/2022] [Indexed: 11/30/2022] Open
Abstract
The ecological and economic importance of forest trees is evident and their survival is necessary to provide the raw materials needed for wood and paper industries, to preserve the diversity of associated animal and plant species, to protect water and soil, and to regulate climate. Forest trees are threatened by anthropogenic factors and biotic and abiotic stresses. Various diseases, including those caused by fungal pathogens, are one of the main threats to forest trees that lead to their dieback. Genomics and transcriptomics studies using next-generation sequencing (NGS) methods can help reveal the architecture of resistance to various diseases and exploit natural genetic diversity to select elite genotypes with high resistance to diseases. In the last two decades, QTL mapping studies led to the identification of QTLs related to disease resistance traits and gene families and transcription factors involved in them, including NB-LRR, WRKY, bZIP and MYB. On the other hand, due to the limitation of recombination events in traditional QTL mapping in families derived from bi-parental crosses, genome-wide association studies (GWAS) that are based on linkage disequilibrium (LD) in unstructured populations overcame these limitations and were able to narrow down QTLs to single genes through genotyping of many individuals using high-throughput markers. Association and QTL mapping studies, by identifying markers closely linked to the target trait, are the prerequisite for marker-assisted selection (MAS) and reduce the breeding period in perennial forest trees. The genomic selection (GS) method uses the information on all markers across the whole genome, regardless of their significance for development of a predictive model for the performance of individuals in relation to a specific trait. GS studies also increase gain per unit of time and dramatically increase the speed of breeding programs. This review article is focused on the progress achieved in the field of dissecting forest tree disease resistance architecture through GWAS and QTL mapping studies. Finally, the merit of methods such as GS in accelerating forest tree breeding programs is also discussed.
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Affiliation(s)
- Mehdi Younessi-Hamzekhanlu
- Department of Forestry and Medicinal Plants, Ahar Faculty of Agriculture and Natural Resources, University of Tabriz, 29 Bahman Blvd., Tabriz P.O. Box 5166616471, Iran
- Correspondence: (M.Y.-H.); (O.G.)
| | - Oliver Gailing
- Department of Forest Genetics and Forest Tree Breeding, University of Göttingen, Büsgenweg 2, D-37077 Göttingen, Germany
- Correspondence: (M.Y.-H.); (O.G.)
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7
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Mishima K, Hirakawa H, Iki T, Fukuda Y, Hirao T, Tamura A, Takahashi M. Comprehensive collection of genes and comparative analysis of full-length transcriptome sequences from Japanese larch (Larix kaempferi) and Kuril larch (Larix gmelinii var. japonica). BMC PLANT BIOLOGY 2022; 22:470. [PMID: 36192701 PMCID: PMC9531402 DOI: 10.1186/s12870-022-03862-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Japanese larch (Larix kaempferi) is an economically important deciduous conifer species that grows in cool-temperate forests and is endemic to Japan. Kuril larch (L. gmelinii var. japonica) is a variety of Dahurian larch that is naturally distributed in the Kuril Islands and Sakhalin. The hybrid larch (L. gmelinii var. japonica × L. kaempferi) exhibits heterosis, which manifests as rapid juvenile growth and high resistance to vole grazing. Since these superior characteristics have been valued by forestry managers, the hybrid larch is one of the most important plantation species in Hokkaido. To accelerate molecular breeding in these species, we collected and compared full-length cDNA isoforms (Iso-Seq) and RNA-Seq short-read, and merged them to construct candidate gene as reference for both Larix species. To validate the results, candidate protein-coding genes (ORFs) related to some flowering signal-related genes were screened from the reference sequences, and the phylogenetic relationship with closely related species was elucidated. RESULTS Using the isoform sequencing of PacBio RS ll and the de novo assembly of RNA-Seq short-read sequences, we identified 50,690 and 38,684 ORFs in Japanese larch and Kuril larch, respectively. BUSCO completeness values were 90.5% and 92.1% in the Japanese and Kuril larches, respectively. After comparing the collected ORFs from the two larch species, a total of 19,813 clusters, comprising 22,571 Japanese larch ORFs and 22,667 Kuril larch ORFs, were contained in the intersection of the Venn diagram. In addition, we screened several ORFs related to flowering signals (SUPPRESSER OF OVEREXPRESSION OF CO1: SOC1, LEAFY: LFY, FLOWERING Locus T: FT, CONSTANCE: CO) from both reference sequences, and very similar found in other species. CONCLUSIONS The collected ORFs will be useful as reference sequences for molecular breeding of Japanese and Kuril larches, and also for clarifying the evolution of the conifer genome and investigating functional genomics.
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Affiliation(s)
- Kentaro Mishima
- Tohoku Regional Breeding Office, Forest Tree Breeding Center, Forestry and Forest Products Research Institute, Forest Research and Management Organization, 95 Osaki, Takizawa, Iwate, 020-0621, Japan.
| | - Hideki Hirakawa
- Kazusa DNA Research Institute, 2-6-7 Kazusa-kamatari, Kisarazu, Chiba, 292-0818, Japan
| | - Taiichi Iki
- Tohoku Regional Breeding Office, Forest Tree Breeding Center, Forestry and Forest Products Research Institute, Forest Research and Management Organization, 95 Osaki, Takizawa, Iwate, 020-0621, Japan
| | - Yoko Fukuda
- Hokkaido Regional Breeding Office, Forest Tree Breeding Center, Forestry and Forest Products Research Institute, Forest Research and Management Organization, 561-1 Bunkyodaimidorimachi, Ebetsu, Hokkaido, 069-0836, Japan
| | - Tomonori Hirao
- Forest Tree Breeding Center, Forestry and Forest Products Research Institute, Forest Research and Management Organization, 3809-1 Ishi, Juo, Hitachi, Ibaraki, 319-1301, Japan
| | - Akira Tamura
- Forest Tree Breeding Center, Forestry and Forest Products Research Institute, Forest Research and Management Organization, 3809-1 Ishi, Juo, Hitachi, Ibaraki, 319-1301, Japan
| | - Makoto Takahashi
- Forest Tree Breeding Center, Forestry and Forest Products Research Institute, Forest Research and Management Organization, 3809-1 Ishi, Juo, Hitachi, Ibaraki, 319-1301, Japan
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Chasing genetic correlation breakers to stimulate population resilience to climate change. Sci Rep 2022; 12:8238. [PMID: 35581288 PMCID: PMC9114142 DOI: 10.1038/s41598-022-12320-3] [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: 11/18/2021] [Accepted: 05/09/2022] [Indexed: 11/29/2022] Open
Abstract
Global climate change introduces new combinations of environmental conditions, which is expected to increase stress on plants. This could affect many traits in multiple ways that are as yet unknown but will likely require the modification of existing genetic relationships among functional traits potentially involved in local adaptation. Theoretical evolutionary studies have determined that it is an advantage to have an excess of recombination events under heterogeneous environmental conditions. Our study, conducted on a population of radiata pine (Pinus radiata D. Don), was able to identify individuals that show high genetic recombination at genomic regions, which potentially include pleiotropic or collocating QTLs responsible for the studied traits, reaching a prediction accuracy of 0.80 in random cross-validation and 0.72 when whole family was removed from the training population and predicted. To identify these highly recombined individuals, a training population was constructed from correlation breakers, created through tandem selection of parents in the previous generation and their consequent mating. Although the correlation breakers showed lower observed heterogeneity possibly due to direct selection in both studied traits, the genomic regions with statistically significant differences in the linkage disequilibrium pattern showed higher level of heretozygosity, which has the effect of decomposing unfavourable genetic correlation. We propose undertaking selection of correlation breakers under current environmental conditions and using genomic predictions to increase the frequency of these ’recombined’ individuals in future plantations, ensuring the resilience of planted forests to changing climates. The increased frequency of such individuals will decrease the strength of the population-level genetic correlations among traits, increasing the opportunity for new trait combinations to be developed in the future.
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9
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Genomic Prediction of Complex Traits in Perennial Plants: A Case for Forest Trees. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2467:493-520. [PMID: 35451788 DOI: 10.1007/978-1-0716-2205-6_18] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
This chapter provides an overview of the genomic selection progress in long-lived forest tree species. Factors affecting the prediction accuracy in genomic prediction are assessed with examples from empirical studies. Infrastructure and resources required for the implementation of genomic selection are evaluated. Some general guidelines are provided for the successful application of genomic selection in forest tree breeding programs.
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Cappa EP, Klutsch JG, Sebastian-Azcona J, Ratcliffe B, Wei X, Da Ros L, Liu Y, Chen C, Benowicz A, Sadoway S, Mansfield SD, Erbilgin N, Thomas BR, El-Kassaby YA. Integrating genomic information and productivity and climate-adaptability traits into a regional white spruce breeding program. PLoS One 2022; 17:e0264549. [PMID: 35298481 PMCID: PMC8929621 DOI: 10.1371/journal.pone.0264549] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 02/13/2022] [Indexed: 11/18/2022] Open
Abstract
Tree improvement programs often focus on improving productivity-related traits; however, under present climate change scenarios, climate change-related (adaptive) traits should also be incorporated into such programs. Therefore, quantifying the genetic variation and correlations among productivity and adaptability traits, and the importance of genotype by environment interactions, including defense compounds involved in biotic and abiotic resistance, is essential for selecting parents for the production of resilient and sustainable forests. Here, we estimated quantitative genetic parameters for 15 growth, wood quality, drought resilience, and monoterpene traits for Picea glauca (Moench) Voss (white spruce). We sampled 1,540 trees from three open-pollinated progeny trials, genotyped with 467,224 SNP markers using genotyping-by-sequencing (GBS). We used the pedigree and SNP information to calculate, respectively, the average numerator and genomic relationship matrices, and univariate and multivariate individual-tree models to obtain estimates of (co)variance components. With few site-specific exceptions, all traits examined were under genetic control. Overall, higher heritability estimates were derived from the genomic- than their counterpart pedigree-based relationship matrix. Selection for height, generally, improved diameter and water use efficiency, but decreased wood density, microfibril angle, and drought resistance. Genome-based correlations between traits reaffirmed the pedigree-based correlations for most trait pairs. High and positive genetic correlations between sites were observed (average 0.68), except for those pairs involving the highest elevation, warmer, and moister site, specifically for growth and microfibril angle. These results illustrate the advantage of using genomic information jointly with productivity and adaptability traits, and defense compounds to enhance tree breeding selection for changing climate.
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Affiliation(s)
- Eduardo P. Cappa
- Instituto de Recursos Biológicos, Centro de Investigación en Recursos Naturales, Instituto Nacional de Tecnología Agropecuaria (INTA), Hurlingham, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Jennifer G. Klutsch
- Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada
| | | | - Blaise Ratcliffe
- Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Xiaojing Wei
- Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada
| | - Letitia Da Ros
- Department of Wood Science, Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Yang Liu
- Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Charles Chen
- Department of Biochemistry and Molecular Biology, Oklahoma State University, Stillwater, Oklahoma, United States of America
| | - Andy Benowicz
- Forest Stewardship and Trade Branch, Alberta Agriculture and Forestry, Edmonton, Alberta, Canada
| | - Shane Sadoway
- Blue Ridge Lumber Inc., West Fraser Mills Ltd, Blue Ridge, Alberta, Canada
| | - Shawn D. Mansfield
- Department of Wood Science, Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Nadir Erbilgin
- Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada
| | - Barb R. Thomas
- Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada
| | - Yousry A. El-Kassaby
- Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Vancouver, British Columbia, Canada
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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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12
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Lauer E, Holland J, Isik F. Prediction ability of genome-wide markers in Pinus taeda L. within and between population is affected by relatedness to the training population and trait genetic architecture. G3 (BETHESDA, MD.) 2022; 12:6440053. [PMID: 34849838 PMCID: PMC9210318 DOI: 10.1093/g3journal/jkab405] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 11/08/2021] [Indexed: 11/26/2022]
Abstract
Genomic prediction has the potential to significantly increase the rate of genetic gain in tree breeding programs. In this study, a clonally replicated population (n = 2063) was used to train a genomic prediction model. The model was validated both within the training population and in a separate population (n = 451). The prediction abilities from random (20% vs 80%) cross validation within the training population were 0.56 and 0.78 for height and stem form, respectively. Removal of all full-sib relatives within the training population resulted in ∼50% reduction in their genomic prediction ability for both traits. The average prediction ability for all 451 individual trees was 0.29 for height and 0.57 for stem form. The degree of genetic linkage (full-sib family, half sib family, unrelated) between the training and validation sets had a strong impact on prediction ability for stem form but not for height. A dominant dwarfing allele, the first to be reported in a conifer species, was discovered via genome-wide association studies on linkage Group 5 that conferred a 0.33-m mean height reduction. However, the QTL was family specific. The rapid decay of linkage disequilibrium, large genome size, and inconsistencies in marker-QTL linkage phase suggest that large, diverse training populations are needed for genomic selection in Pinus taeda L.
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Affiliation(s)
- Edwin Lauer
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, USA
| | - James Holland
- USDA-ARS Plant Science Research Unit, North Carolina State University, Raleigh, NC 27695, USA
| | - Fikret Isik
- Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, USA
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13
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Perry A, Wachowiak W, Beaton J, Iason G, Cottrell J, Cavers S. Identifying and testing marker‐trait associations for growth and phenology in three pine species: implications for genomic prediction. Evol Appl 2022; 15:330-348. [PMID: 35233251 PMCID: PMC8867712 DOI: 10.1111/eva.13345] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 12/08/2021] [Accepted: 12/09/2021] [Indexed: 12/02/2022] Open
Abstract
In tree species, genomic prediction offers the potential to forecast mature trait values in early growth stages, if robust marker–trait associations can be identified. Here we apply a novel multispecies approach using genotypes from a new genotyping array, based on 20,795 single nucleotide polymorphisms (SNPs) from three closely related pine species (Pinus sylvestris, Pinus uncinata and Pinus mugo), to test for associations with growth and phenology data from a common garden study. Predictive models constructed using significantly associated SNPs were then tested and applied to an independent multisite field trial of P. sylvestris and the capability to predict trait values was evaluated. One hundred and eighteen SNPs showed significant associations with the traits in the pine species. Common SNPs (MAF > 0.05) associated with bud set were only found in genes putatively involved in growth and development, whereas those associated with growth and budburst were also located in genes putatively involved in response to environment and, to a lesser extent, reproduction. At one of the two independent sites, the model we developed produced highly significant correlations between predicted values and observed height data (YA, height 2020: r = 0.376, p < 0.001). Predicted values estimated with our budburst model were weakly but positively correlated with duration of budburst at one of the sites (GS, 2015: r = 0.204, p = 0.034; 2018: r = 0.205, p = 0.034–0.037) and negatively associated with budburst timing at the other (YA: r = −0.202, p = 0.046). Genomic prediction resulted in the selection of sets of trees whose mean height was taller than the average for each site. Our results provide tentative support for the capability of prediction models to forecast trait values in trees, while highlighting the need for caution in applying them to trees grown in different environments.
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Affiliation(s)
- Annika Perry
- UK Centre for Ecology & Hydrology Edinburgh Penicuik Midlothian EH26 0QB UK
| | - Witold Wachowiak
- Institute of Environmental Biology Faculty of Biology Adam Mickiewicz University Poznań Poland
| | - Joan Beaton
- James Hutton Institute Craigiebuckler, Aberdeen AB15 8QH UK
| | - Glenn Iason
- James Hutton Institute Craigiebuckler, Aberdeen AB15 8QH UK
| | - Joan Cottrell
- Northern Research Station, Forest Research Roslin EH25 9SY UK
| | - Stephen Cavers
- UK Centre for Ecology & Hydrology Edinburgh Penicuik Midlothian EH26 0QB UK
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14
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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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15
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McGaugh SE, Lorenz AJ, Flagel LE. The utility of genomic prediction models in evolutionary genetics. Proc Biol Sci 2021; 288:20210693. [PMID: 34344180 PMCID: PMC8334854 DOI: 10.1098/rspb.2021.0693] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 07/15/2021] [Indexed: 12/25/2022] Open
Abstract
Variation in complex traits is the result of contributions from many loci of small effect. Based on this principle, genomic prediction methods are used to make predictions of breeding value for an individual using genome-wide molecular markers. In breeding, genomic prediction models have been used in plant and animal breeding for almost two decades to increase rates of genetic improvement and reduce the length of artificial selection experiments. However, evolutionary genomics studies have been slow to incorporate this technique to select individuals for breeding in a conservation context or to learn more about the genetic architecture of traits, the genetic value of missing individuals or microevolution of breeding values. Here, we outline the utility of genomic prediction and provide an overview of the methodology. We highlight opportunities to apply genomic prediction in evolutionary genetics of wild populations and the best practices when using these methods on field-collected phenotypes.
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Affiliation(s)
- Suzanne E. McGaugh
- Ecology, Evolution, and Behavior, University of Minnesota, 140 Gortner Lab, 1479 Gortner Avenue, Saint Paul, MN 55108, USA
| | - Aaron J. Lorenz
- Agronomy and Plant Genetics, University of Minnesota, 411 Borlaug Hall, 1991 Upper Buford Circle, Saint Paul, MN 55108, USA
| | - Lex E. Flagel
- Plant and Microbial Biology, University of Minnesota, 140 Gortner Lab, 1479 Gortner Avenue, Saint Paul, MN 55108, USA
- Bayer Crop Science, 700 W Chesterfield Parkway, Chesterfield, MO 63017, USA
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16
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Depardieu C, Gérardi S, Nadeau S, Parent GJ, Mackay J, Lenz P, Lamothe M, Girardin MP, Bousquet J, Isabel N. Connecting tree-ring phenotypes, genetic associations and transcriptomics to decipher the genomic architecture of drought adaptation in a widespread conifer. Mol Ecol 2021; 30:3898-3917. [PMID: 33586257 PMCID: PMC8451828 DOI: 10.1111/mec.15846] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 01/15/2021] [Accepted: 01/27/2021] [Indexed: 01/02/2023]
Abstract
As boreal forests face significant threats from climate change, understanding evolutionary trajectories of coniferous species has become fundamental to adapting management and conservation to a drying climate. We examined the genomic architecture underlying adaptive variation related to drought tolerance in 43 populations of a widespread boreal conifer, white spruce (Piceaglauca [Moench] Voss), by combining genotype–environment associations, genotype–phenotype associations, and transcriptomics. Adaptive genetic variation was identified by correlating allele frequencies for 6,153 single nucleotide polymorphisms from 2,606 candidate genes with temperature, precipitation and aridity gradients, and testing for significant associations between genotypes and 11 dendrometric and drought‐related traits (i.e., anatomical, growth response and climate‐sensitivity traits) using a polygenic model. We identified a set of 285 genes significantly associated with a climatic factor or a phenotypic trait, including 110 that were differentially expressed in response to drought under greenhouse‐controlled conditions. The interlinked phenotype–genotype–environment network revealed eight high‐confidence genes involved in white spruce adaptation to drought, of which four were drought‐responsive in the expression analysis. Our findings represent a significant step toward the characterization of the genomic basis of drought tolerance and adaptation to climate in conifers, which is essential to enable the establishment of resilient forests in view of new climate conditions. see also the Perspective by Lars Opgenoorth and Christian Rellstab
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Affiliation(s)
- Claire Depardieu
- Canada Research Chair in Forest GenomicsInstitute for Systems and Integrative BiologyUniversité LavalQuébecQCCanada
- Centre for Forest ResearchDépartement des sciences du bois et de la forêtUniversité LavalQuébecQCCanada
- Natural Resources CanadaCanadian Forest ServiceLaurentian Forestry CenterQuébecQCCanada
| | - Sébastien Gérardi
- Canada Research Chair in Forest GenomicsInstitute for Systems and Integrative BiologyUniversité LavalQuébecQCCanada
- Centre for Forest ResearchDépartement des sciences du bois et de la forêtUniversité LavalQuébecQCCanada
| | - Simon Nadeau
- Natural Resources CanadaCanadian Forest ServiceCanadian Wood Fibre CenterQuébecQCCanada
| | - Geneviève J. Parent
- Laboratory of GenomicsMaurice‐Lamontagne Institute, Fisheries and Oceans CanadaMont‐JoliQCCanada
| | - John Mackay
- Canada Research Chair in Forest GenomicsInstitute for Systems and Integrative BiologyUniversité LavalQuébecQCCanada
- Department of Plant SciencesUniversity of OxfordOxfordUK
| | - Patrick Lenz
- Canada Research Chair in Forest GenomicsInstitute for Systems and Integrative BiologyUniversité LavalQuébecQCCanada
- Natural Resources CanadaCanadian Forest ServiceCanadian Wood Fibre CenterQuébecQCCanada
| | - Manuel Lamothe
- Canada Research Chair in Forest GenomicsInstitute for Systems and Integrative BiologyUniversité LavalQuébecQCCanada
- Natural Resources CanadaCanadian Forest ServiceLaurentian Forestry CenterQuébecQCCanada
| | - Martin P. Girardin
- Natural Resources CanadaCanadian Forest ServiceLaurentian Forestry CenterQuébecQCCanada
- Centre for Forest ResearchUniversité du Québec à MontréalMontréalQCCanada
| | - Jean Bousquet
- Canada Research Chair in Forest GenomicsInstitute for Systems and Integrative BiologyUniversité LavalQuébecQCCanada
- Centre for Forest ResearchDépartement des sciences du bois et de la forêtUniversité LavalQuébecQCCanada
| | - Nathalie Isabel
- Canada Research Chair in Forest GenomicsInstitute for Systems and Integrative BiologyUniversité LavalQuébecQCCanada
- Centre for Forest ResearchDépartement des sciences du bois et de la forêtUniversité LavalQuébecQCCanada
- Natural Resources CanadaCanadian Forest ServiceLaurentian Forestry CenterQuébecQCCanada
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17
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Bernhardsson C, Zan Y, Chen Z, Ingvarsson PK, Wu HX. Development of a highly efficient 50K single nucleotide polymorphism genotyping array for the large and complex genome of Norway spruce (Picea abies L. Karst) by whole genome resequencing and its transferability to other spruce species. Mol Ecol Resour 2020; 21:880-896. [PMID: 33179386 PMCID: PMC7984398 DOI: 10.1111/1755-0998.13292] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 10/23/2020] [Accepted: 11/04/2020] [Indexed: 12/30/2022]
Abstract
Norway spruce (Picea abies L. Karst) is one of the most important forest tree species with significant economic and ecological impact in Europe. For decades, genomic and genetic studies on Norway spruce have been challenging due to the large and repetitive genome (19.6 Gb with more than 70% being repetitive). To accelerate genomic studies, including population genetics, genome‐wide association studies (GWAS) and genomic selection (GS), in Norway spruce and related species, we here report on the design and performance of a 50K single nucleotide polymorphism (SNP) genotyping array for Norway spruce. The array is developed based on whole genome resequencing (WGS), making it the first WGS‐based SNP array in any conifer species so far. After identifying SNPs using genome resequencing data from 29 trees collected in northern Europe, we adopted a two‐step approach to design the array. First, we built a 450K screening array and used this to genotype a population of 480 trees sampled from both natural and breeding populations across the Norway spruce distribution range. These samples were then used to select high‐confidence probes that were put on the final 50K array. The SNPs selected are distributed over 45,552 scaffolds from the P. abies version 1.0 genome assembly and target 19,954 unique gene models with an even coverage of the 12 linkage groups in Norway spruce. We show that the array has a 99.5% probe specificity, >98% Mendelian allelic inheritance concordance, an average sample call rate of 96.30% and an SNP call rate of 98.90% in family trios and haploid tissues. We also observed that 23,797 probes (50%) could be identified with high confidence in three other spruce species (white spruce [Picea glauca], black spruce [P. mariana] and Sitka spruce [P. sitchensis]). The high‐quality genotyping array will be a valuable resource for genetic and genomic studies in Norway spruce as well as in other conifer species of the same genus.
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Affiliation(s)
- Carolina Bernhardsson
- Department of Ecology and Environmental Science, Umeå University, Umeå, Sweden.,Department of Organismal Biology, Uppsala University, Uppsala, Sweden
| | - Yanjun Zan
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Science, Umeå, Sweden
| | - Zhiqiang Chen
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Science, Umeå, Sweden
| | - Pär K Ingvarsson
- Linnean Centre for Plant Biology, Department of Plant Biology, Uppsala BioCenter, Swedish University of Agricultural Science, Uppsala, Sweden
| | - Harry X Wu
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Science, Umeå, Sweden.,Beijing Advanced Innovation Centre for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China.,Black Mountain Laboratory, CSIRO National Research Collection Australia, Canberra, ACT, Australia
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18
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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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19
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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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20
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Genomic Selection for Forest Tree Improvement: Methods, Achievements and Perspectives. FORESTS 2020. [DOI: 10.3390/f11111190] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The breeding of forest trees is only a few decades old, and is a much more complicated, longer, and expensive endeavor than the breeding of agricultural crops. One breeding cycle for forest trees can take 20–30 years. Recent advances in genomics and molecular biology have revolutionized traditional plant breeding based on visual phenotype assessment: the development of different types of molecular markers has made genotype selection possible. Marker-assisted breeding can significantly accelerate the breeding process, but this method has not been shown to be effective for selection of complex traits on forest trees. This new method of genomic selection is based on the analysis of all effects of quantitative trait loci (QTLs) using a large number of molecular markers distributed throughout the genome, which makes it possible to assess the genomic estimated breeding value (GEBV) of an individual. This approach is expected to be much more efficient for forest tree improvement than traditional breeding. Here, we review the current state of the art in the application of genomic selection in forest tree breeding and discuss different methods of genotyping and phenotyping. We also compare the accuracies of genomic prediction models and highlight the importance of a prior cost-benefit analysis before implementing genomic selection. Perspectives for the further development of this approach in forest breeding are also discussed: expanding the range of species and the list of valuable traits, the application of high-throughput phenotyping methods, and the possibility of using epigenetic variance to improve of forest trees.
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21
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Ravelombola WS, Qin J, Shi A, Nice L, Bao Y, Lorenz A, Orf JH, Young ND, Chen S. Genome-wide association study and genomic selection for tolerance of soybean biomass to soybean cyst nematode infestation. PLoS One 2020; 15:e0235089. [PMID: 32673346 PMCID: PMC7365597 DOI: 10.1371/journal.pone.0235089] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 06/08/2020] [Indexed: 02/07/2023] Open
Abstract
Soybean cyst nematode (SCN), Heterodera glycines Ichinohe, is one of the most devastating pathogens affecting soybean production in the U.S. and worldwide. The use of SCN-resistant soybean cultivars is one of the most affordable strategies to cope with SCN infestation. Because of the limited sources of SCN resistance and changes in SCN virulence phenotypes, host resistance in current cultivars has increasingly been overcome by the pathogen. Host tolerance has been recognized as an additional tool to manage the SCN. The objectives of this study were to conduct a genome-wide association study (GWAS), to identify single nucleotide polymorphism (SNP) markers, and to perform a genomic selection (GS) study for SCN tolerance in soybean based on reduction in biomass. A total of 234 soybean genotypes (lines) were evaluated for their tolerance to SCN in greenhouse using four replicates. The tolerance index (TI = 100 × Biomass of a line in SCN infested / Biomass of the line without SCN) was used as phenotypic data of SCN tolerance. GWAS was conducted using a total of 3,782 high quality SNPs. GS was performed based upon the whole set of SNPs and the GWAS-derived SNPs, respectively. Results showed that (1) a large variation in soybean TI to SCN infection among the soybean genotypes was identified; (2) a total of 35, 21, and 6 SNPs were found to be associated with SCN tolerance using the models SMR, GLM (PCA), and MLM (PCA+K) with 6 SNPs overlapping between models; (3) GS accuracy was SNP set-, model-, and training population size-dependent; and (4) genes around Glyma.06G134900, Glyma.15G097500.1, Glyma.15G100900.3, Glyma.15G105400, Glyma.15G107200, and Glyma.19G121200.1 (Table 4). Glyma.06G134900, Glyma.15G097500.1, Glyma.15G100900.3, Glyma.15G105400, and Glyma.19G121200.1 are best candidates. To the best of our knowledge, this is the first report highlighting SNP markers associated with tolerance index based on biomass reduction under SCN infestation in soybean. This research opens a new approach to use SCN tolerance in soybean breeding and the SNP markers will provide a tool for breeders to select for SCN tolerance.
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Affiliation(s)
| | - Jun Qin
- Department of Horticulture, PTSC316, University of Arkansas, Fayetteville, AR, United States of America
- Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, Hebei, China
| | - Ainong Shi
- Department of Horticulture, PTSC316, University of Arkansas, Fayetteville, AR, United States of America
| | - Liana Nice
- Southern Research & Outreach Center, University of Minnesota, Waseca, MN, United States of America
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States of America
| | - Yong Bao
- Southern Research & Outreach Center, University of Minnesota, Waseca, MN, United States of America
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States of America
| | - Aaron Lorenz
- Southern Research & Outreach Center, University of Minnesota, Waseca, MN, United States of America
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States of America
| | - James H Orf
- Southern Research & Outreach Center, University of Minnesota, Waseca, MN, United States of America
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States of America
| | - Nevin D Young
- Department of Plant Pathology, University of Minnesota, St. Paul, MN, United States of America
| | - Senyu Chen
- Southern Research & Outreach Center, University of Minnesota, Waseca, MN, United States of America
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States of America
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Depardieu C, Girardin MP, Nadeau S, Lenz P, Bousquet J, Isabel N. Adaptive genetic variation to drought in a widely distributed conifer suggests a potential for increasing forest resilience in a drying climate. THE NEW PHYTOLOGIST 2020; 227:427-439. [PMID: 32173867 PMCID: PMC7317761 DOI: 10.1111/nph.16551] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 02/29/2020] [Indexed: 05/03/2023]
Abstract
Drought intensity and frequency are increasing under global warming, with soil water availability now being a major factor limiting tree growth in circumboreal forests. Still, the adaptive capacity of trees in the face of future climatic regimes remains poorly documented. Using 1481 annually resolved tree-ring series from 29-yr-old trees, we evaluated the drought sensitivity of 43 white spruce (Picea glauca (Moench) Voss) populations established in a common garden experiment. We show that genetic variation among populations in response to drought plays a significant role in growth resilience. Local genetic adaptation allowed populations from drier geographical origins to grow better, as indicated by higher resilience to extreme drought events, compared with populations from more humid geographical origins. The substantial genetic variation found for growth resilience highlights the possibility of selecting for drought resilience in boreal conifers. As a major research outcome, we showed that adaptive genetic variation in response to changing local conditions can shape drought vulnerability at the intraspecific level. Our findings have wide implications for forest ecosystem management and conservation.
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Affiliation(s)
- Claire Depardieu
- Natural Resources CanadaCanadian Forest ServiceLaurentian Forestry Centre1055, rue du PEPS, PO Box 10380, Stn. Sainte‐FoyQuébecQCG1V 4C7Canada
- Canada Research Chair in Forest GenomicsInstitute for Systems and Integrative BiologyUniversité LavalQuébecQCG1V 0A6Canada
| | - Martin P. Girardin
- Natural Resources CanadaCanadian Forest ServiceLaurentian Forestry Centre1055, rue du PEPS, PO Box 10380, Stn. Sainte‐FoyQuébecQCG1V 4C7Canada
| | - Simon Nadeau
- Natural Resources CanadaCanadian Forest ServiceCanadian Wood Fibre Centre1055, rue du PEPS, PO Box 10380, Stn. Sainte‐FoyQuébecQCG1V 4C7Canada
| | - Patrick Lenz
- Canada Research Chair in Forest GenomicsInstitute for Systems and Integrative BiologyUniversité LavalQuébecQCG1V 0A6Canada
- Natural Resources CanadaCanadian Forest ServiceCanadian Wood Fibre Centre1055, rue du PEPS, PO Box 10380, Stn. Sainte‐FoyQuébecQCG1V 4C7Canada
| | - Jean Bousquet
- Canada Research Chair in Forest GenomicsInstitute for Systems and Integrative BiologyUniversité LavalQuébecQCG1V 0A6Canada
| | - Nathalie Isabel
- Natural Resources CanadaCanadian Forest ServiceLaurentian Forestry Centre1055, rue du PEPS, PO Box 10380, Stn. Sainte‐FoyQuébecQCG1V 4C7Canada
- Canada Research Chair in Forest GenomicsInstitute for Systems and Integrative BiologyUniversité LavalQuébecQCG1V 0A6Canada
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Linkage disequilibrium vs. pedigree: Genomic selection prediction accuracy in conifer species. PLoS One 2020; 15:e0232201. [PMID: 32520936 PMCID: PMC7286500 DOI: 10.1371/journal.pone.0232201] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 04/08/2020] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND The presupposition of genomic selection (GS) is that predictive accuracies should be based on population-wide linkage disequilibrium (LD). However, in species with large, highly complex genomes the limitation of marker density may preclude the ability to resolve LD accurately enough for GS. Here we investigate such an effect in two conifer species with ~ 20 Gbp genomes, Douglas-fir (Pseudotsuga menziesii Mirb. (Franco)) and Interior spruce (Picea glauca (Moench) Voss x Picea engelmannii Parry ex Engelm.). Random sampling of markers was performed to obtain SNP sets with totals in the range of 200-50,000, this was replicated 10 times. Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) was deployed as the GS method to test these SNP sets, and 10-fold cross-validation was performed on 1,321 Douglas-fir trees, representing 37 full-sib F1 families and on 1,126 Interior spruce trees, representing 25 open-pollinated (half-sib) families. Both trials are located on 3 sites in British Columbia, Canada. RESULTS As marker number increased, so did GS predictive accuracy for both conifer species. However, a plateau in the gain of accuracy became apparent around 10,000-15,000 markers for both Douglas-fir and Interior spruce. Despite random marker selection, little variation in predictive accuracy was observed across replications. On average, Douglas-fir prediction accuracies were higher than those of Interior spruce, reflecting the difference between full- and half-sib families for Douglas-fir and Interior spruce populations, respectively, as well as their respective effective population size. CONCLUSIONS Although possibly advantageous within an advanced breeding population, reducing marker density cannot be recommended for carrying out GS in conifers. Significant LD between markers and putative causal variants was not detected using 50,000 SNPS, and GS was enabled only through the tracking of relatedness in the populations studied. Dramatically increasing marker density would enable said markers to better track LD with causal variants in these large, genetically diverse genomes; as well as providing a model that could be used across populations, breeding programs, and traits.
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Klápště J, Dungey HS, Graham NJ, Telfer EJ. Effect of trait's expression level on single-step genomic evaluation of resistance to Dothistroma needle blight. BMC PLANT BIOLOGY 2020; 20:205. [PMID: 32393229 DOI: 10.1186/s12870-12020-02403-12876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 04/23/2020] [Indexed: 05/28/2023]
Abstract
BACKGROUND Many conifer breeding programs are paying increasing attention to breeding for resistance to needle disease due to the increasing importance of climate change. Phenotyping of traits related to resistance has many biological and temporal constraints that can often confound the ability to achieve reliable phenotypes and consequently, reliable genetic progress. The development of next generation sequencing platforms has also enabled implementation of genomic approaches in species lacking robust reference genomes. Genomic selection is, therefore, a promising strategy to overcome the constraints of needle disease phenotyping. RESULTS We found high accuracy in the prediction of genomic breeding values in the disease-related traits that were well characterized, reaching 0.975 for genotyped individuals and 0.587 for non-genotyped individuals. This compared well with pedigree-based accuracies of up to 0.746. Surprisingly, poorly phenotyped disease traits also showed very high accuracy in terms of correlation of predicted genomic breeding values with pedigree-based counterparts. However, this was likely caused by the fact that both were clustered around the population mean, while deviations from the population mean caused by genetic effects did not appear to be well described. Caution should therefore be taken with the interpretation of results in poorly phenotyped traits. CONCLUSIONS Implementation of genomic selection in this test population of Pinus radiata resulted in a relatively high prediction accuracy of needle loss due to Dothistroma septosporum compared with a pedigree-based approach. Using genomics to avoid biological/temporal constraints where phenotyping is reliable appears promising. Unsurprisingly, reliable phenotyping, resulting in good heritability estimates, is a fundamental requirement for the development of a reliable prediction model. Furthermore, our results are also specific to the single pathogen mating-type that is present in New Zealand, and may change with future incursion of other pathogen varieties. There is no doubt, however, that once a robust genomic prediction model is built, it will be invaluable to not only select for host tolerance, but for other economically important traits simultaneously. This tool will thus future-proof our forests by mitigating the risk of disease outbreaks induced by future changes in climate.
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Affiliation(s)
- Jaroslav Klápště
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010, New Zealand.
| | - Heidi S Dungey
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010, New Zealand
| | - Natalie J Graham
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010, New Zealand
| | - Emily J Telfer
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010, New Zealand
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25
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Klápště J, Dungey HS, Graham NJ, Telfer EJ. Effect of trait's expression level on single-step genomic evaluation of resistance to Dothistroma needle blight. BMC PLANT BIOLOGY 2020; 20:205. [PMID: 32393229 PMCID: PMC7216529 DOI: 10.1186/s12870-020-02403-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 04/23/2020] [Indexed: 05/23/2023]
Abstract
BACKGROUND Many conifer breeding programs are paying increasing attention to breeding for resistance to needle disease due to the increasing importance of climate change. Phenotyping of traits related to resistance has many biological and temporal constraints that can often confound the ability to achieve reliable phenotypes and consequently, reliable genetic progress. The development of next generation sequencing platforms has also enabled implementation of genomic approaches in species lacking robust reference genomes. Genomic selection is, therefore, a promising strategy to overcome the constraints of needle disease phenotyping. RESULTS We found high accuracy in the prediction of genomic breeding values in the disease-related traits that were well characterized, reaching 0.975 for genotyped individuals and 0.587 for non-genotyped individuals. This compared well with pedigree-based accuracies of up to 0.746. Surprisingly, poorly phenotyped disease traits also showed very high accuracy in terms of correlation of predicted genomic breeding values with pedigree-based counterparts. However, this was likely caused by the fact that both were clustered around the population mean, while deviations from the population mean caused by genetic effects did not appear to be well described. Caution should therefore be taken with the interpretation of results in poorly phenotyped traits. CONCLUSIONS Implementation of genomic selection in this test population of Pinus radiata resulted in a relatively high prediction accuracy of needle loss due to Dothistroma septosporum compared with a pedigree-based approach. Using genomics to avoid biological/temporal constraints where phenotyping is reliable appears promising. Unsurprisingly, reliable phenotyping, resulting in good heritability estimates, is a fundamental requirement for the development of a reliable prediction model. Furthermore, our results are also specific to the single pathogen mating-type that is present in New Zealand, and may change with future incursion of other pathogen varieties. There is no doubt, however, that once a robust genomic prediction model is built, it will be invaluable to not only select for host tolerance, but for other economically important traits simultaneously. This tool will thus future-proof our forests by mitigating the risk of disease outbreaks induced by future changes in climate.
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Affiliation(s)
- Jaroslav Klápště
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010 New Zealand
| | - Heidi S. Dungey
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010 New Zealand
| | - Natalie J. Graham
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010 New Zealand
| | - Emily J. Telfer
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010 New Zealand
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Bartholomé J, Brachi B, Marçais B, Mougou-Hamdane A, Bodénès C, Plomion C, Robin C, Desprez-Loustau ML. The genetics of exapted resistance to two exotic pathogens in pedunculate oak. THE NEW PHYTOLOGIST 2020; 226:1088-1103. [PMID: 31711257 DOI: 10.1111/nph.16319] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 11/05/2019] [Indexed: 05/16/2023]
Abstract
Exotic pathogens cause severe damage in natural populations in the absence of coevolutionary dynamics with their hosts. However, some resistance to such pathogens may occur in naive populations. The objective of this study was to investigate the genetics of this so-called 'exapted' resistance to two pathogens of Asian origin (Erysiphe alphitoides and Phytophthora cinnamomi) in European oak. Host-pathogen compatibility was assessed by recording infection success and pathogen growth in a full-sib family of Quercus robur under controlled and natural conditions. Two high-resolution genetic maps anchored on the reference genome were used to study the genetic architecture of resistance and to identify positional candidate genes. Two genomic regions, each containing six strong and stable quantitative trait loci (QTLs) accounting for 12-19% of the phenotypic variation, were mainly associated with E. alphitoides infection. Candidate genes, especially genes encoding receptor-like-kinases and galactinol synthases, were identified in these regions. The three QTLs associated with P. cinnamomi infection did not colocate with QTLs found for E. alphitoides. These findings provide evidence that exapted resistance to E. alphitoides and P. cinnamomi is present in Q. robur and suggest that the underlying molecular mechanisms involve genes encoding proteins with extracellular signaling functions.
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Affiliation(s)
- Jérôme Bartholomé
- BIOGECO, INRA, Université de Bordeaux, 69 route d'Arcachon, Cestas, 33610, France
- AGAP, Université de Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, 34398, France
- CIRAD, UMR AGAP, TA A-108 / 03 - Avenue Agropolis, Montpellier, 34398, France
| | - Benjamin Brachi
- BIOGECO, INRA, Université de Bordeaux, 69 route d'Arcachon, Cestas, 33610, France
| | - Benoit Marçais
- IAM, INRA, Université de Lorraine, Champenoux, Nancy, 54000, France
| | - Amira Mougou-Hamdane
- BIOGECO, INRA, Université de Bordeaux, 69 route d'Arcachon, Cestas, 33610, France
- Institut National Agronomique de Tunisie, Université de Carthage, 43 avenue Charles Nicolle Cité el Mahrajène, Tunis, 1082, Tunisia
| | - Catherine Bodénès
- BIOGECO, INRA, Université de Bordeaux, 69 route d'Arcachon, Cestas, 33610, France
| | - Christophe Plomion
- BIOGECO, INRA, Université de Bordeaux, 69 route d'Arcachon, Cestas, 33610, France
| | - Cécile Robin
- BIOGECO, INRA, Université de Bordeaux, 69 route d'Arcachon, Cestas, 33610, France
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27
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Zhou L, Chen Z, Olsson L, Grahn T, Karlsson B, Wu HX, Lundqvist SO, García-Gil MR. Effect of number of annual rings and tree ages on genomic predictive ability for solid wood properties of Norway spruce. BMC Genomics 2020; 21:323. [PMID: 32334511 PMCID: PMC7183120 DOI: 10.1186/s12864-020-6737-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 04/15/2020] [Indexed: 12/15/2022] Open
Abstract
Background Genomic selection (GS) or genomic prediction is considered as a promising approach to accelerate tree breeding and increase genetic gain by shortening breeding cycle, but the efforts to develop routines for operational breeding are so far limited. We investigated the predictive ability (PA) of GS based on 484 progeny trees from 62 half-sib families in Norway spruce (Picea abies (L.) Karst.) for wood density, modulus of elasticity (MOE) and microfibril angle (MFA) measured with SilviScan, as well as for measurements on standing trees by Pilodyn and Hitman instruments. Results GS predictive abilities were comparable with those based on pedigree-based prediction. Marker-based PAs were generally 25–30% higher for traits density, MFA and MOE measured with SilviScan than for their respective standing tree-based method which measured with Pilodyn and Hitman. Prediction accuracy (PC) of the standing tree-based methods were similar or even higher than increment core-based method. 78–95% of the maximal PAs of density, MFA and MOE obtained from coring to the pith at high age were reached by using data possible to obtain by drilling 3–5 rings towards the pith at tree age 10–12. Conclusions This study indicates standing tree-based measurements is a cost-effective alternative method for GS. PA of GS methods were comparable with those pedigree-based prediction. The highest PAs were reached with at least 80–90% of the dataset used as training set. Selection for trait density could be conducted at an earlier age than for MFA and MOE. Operational breeding can also be optimized by training the model at an earlier age or using 3 to 5 outermost rings at tree age 10 to 12 years, thereby shortening the cycle and reducing the impact on the tree.
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Affiliation(s)
- Linghua Zhou
- Department of Forest Genetics and Plant physiology, Umeå Plant Science Centre, Swedish University of Agricultural Sciences, SE-901 83, Umeå, Sweden
| | - Zhiqiang Chen
- Department of Forest Genetics and Plant physiology, Umeå Plant Science Centre, Swedish University of Agricultural Sciences, SE-901 83, Umeå, Sweden
| | - Lars Olsson
- RISE Bioeconomy, Box 5604, SE-114 86, Stockholm, Sweden
| | - Thomas Grahn
- RISE Bioeconomy, Box 5604, SE-114 86, Stockholm, Sweden
| | - Bo Karlsson
- Skogforsk, Ekebo 2250, SE-268 90, Svalöv, Sweden
| | - Harry X Wu
- Department of Forest Genetics and Plant physiology, Umeå Plant Science Centre, Swedish University of Agricultural Sciences, SE-901 83, Umeå, Sweden.,Beijing Advanced Innovation Centre for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, China.,CSIRO National Collection Research Australia, Black Mountain Laboratory, ACT, Canberra, 2601, Australia
| | - Sven-Olof Lundqvist
- RISE Bioeconomy, Box 5604, SE-114 86, Stockholm, Sweden.,, IIC, Rosenlundsgatan 48B, SE-118 63, Stockholm, Sweden
| | - María Rosario García-Gil
- Department of Forest Genetics and Plant physiology, Umeå Plant Science Centre, Swedish University of Agricultural Sciences, SE-901 83, Umeå, Sweden.
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Potential of Genome-Wide Association Studies and Genomic Selection to Improve Productivity and Quality of Commercial Timber Species in Tropical Rainforest, a Case Study of Shorea platyclados. FORESTS 2020. [DOI: 10.3390/f11020239] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Shorea platyclados (Dark Red Meranti) is a commercially important timber tree species in Southeast Asia. However, its stocks have dramatically declined due, inter alia, to excessive logging, insufficient natural regeneration and a slow recovery rate. Thus, there is a need to promote enrichment planting and develop effective technique to support its rehabilitation and improve timber production through implementation of Genome-Wide Association Studies (GWAS) and Genomic Selection (GS). To assist such efforts, plant materials were collected from a half-sib progeny population in Sari Bumi Kusuma forest concession, Kalimantan, Indonesia. Using 5900 markers in sequences obtained from 356 individuals, we detected high linkage disequilibrium (LD) extending up to >145 kb, suggesting that associations between phenotypic traits and markers in LD can be more easily and feasibly detected with GWAS than with analysis of quantitative trait loci (QTLs). However, the detection power of GWAS seems low, since few single nucleotide polymorphisms linked to any focal traits were detected with a stringent false discovery rate, indicating that the species’ phenotypic traits are mostly under polygenic quantitative control. Furthermore, Machine Learning provided higher prediction accuracies than Bayesian methods. We also found that stem diameter, branch diameter ratio and wood density were more predictable than height, clear bole, branch angle and wood stiffness traits. Our study suggests that GS has potential for improving the productivity and quality of S. platyclados, and our genomic heritability estimates may improve the selection of traits to target in future breeding of this species.
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29
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Lenz PRN, Nadeau S, Mottet M, Perron M, Isabel N, Beaulieu J, Bousquet J. Multi-trait genomic selection for weevil resistance, growth, and wood quality in Norway spruce. Evol Appl 2020; 13:76-94. [PMID: 31892945 PMCID: PMC6935592 DOI: 10.1111/eva.12823] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 04/18/2019] [Accepted: 05/15/2019] [Indexed: 12/12/2022] Open
Abstract
Plantation-grown trees have to cope with an increasing pressure of pest and disease in the context of climate change, and breeding approaches using genomics may offer efficient and flexible tools to face this pressure. In the present study, we targeted genetic improvement of resistance of an introduced conifer species in Canada, Norway spruce (Picea abies (L.) Karst.), to the native white pine weevil (Pissodes strobi Peck). We developed single- and multi-trait genomic selection (GS) models and selection indices considering the relationships between weevil resistance, intrinsic wood quality, and growth traits. Weevil resistance, acoustic velocity as a proxy for mechanical wood stiffness, and average wood density showed moderate-to-high heritability and low genotype-by-environment interactions. Weevil resistance was genetically positively correlated with tree height, height-to-diameter at breast height (DBH) ratio, and acoustic velocity. The accuracy of the different GS models tested (GBLUP, threshold GBLUP, Bayesian ridge regression, BayesCπ) was high and did not differ among each other. Multi-trait models performed similarly as single-trait models when all trees were phenotyped. However, when weevil attack data were not available for all trees, weevil resistance was more accurately predicted by integrating genetically correlated growth traits into multi-trait GS models. A GS index that corresponded to the breeders' priorities achieved near maximum gains for weevil resistance, acoustic velocity, and height growth, but a small decrease for DBH. The results of this study indicate that it is possible to breed for high-quality, weevil-resistant Norway spruce reforestation stock with high accuracy achieved from single-trait or multi-trait GS.
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Affiliation(s)
- Patrick R. N. Lenz
- Canadian Wood Fibre CentreNatural Resources CanadaQuébecQuébecCanada
- Canada Research Chair in Forest GenomicsInstitute of Integrative Biology and Systems, Centre for Forest ResearchUniversité LavalQuébecQuébecCanada
| | - Simon Nadeau
- Canadian Wood Fibre CentreNatural Resources CanadaQuébecQuébecCanada
| | - Marie‐Josée Mottet
- Ministère des Forêts, de la Faune et des ParcsGouvernement du Québec, Direction de la recherche forestièreQuébecQuébecCanada
| | - Martin Perron
- Canada Research Chair in Forest GenomicsInstitute of Integrative Biology and Systems, Centre for Forest ResearchUniversité LavalQuébecQuébecCanada
- Ministère des Forêts, de la Faune et des ParcsGouvernement du Québec, Direction de la recherche forestièreQuébecQuébecCanada
| | - Nathalie Isabel
- Canada Research Chair in Forest GenomicsInstitute of Integrative Biology and Systems, Centre for Forest ResearchUniversité LavalQuébecQuébecCanada
- Laurentian Forestry CentreNatural Resources CanadaQuébecQuébecCanada
| | - Jean Beaulieu
- Canada Research Chair in Forest GenomicsInstitute of Integrative Biology and Systems, Centre for Forest ResearchUniversité LavalQuébecQuébecCanada
| | - Jean Bousquet
- Canada Research Chair in Forest GenomicsInstitute of Integrative Biology and Systems, Centre for Forest ResearchUniversité LavalQuébecQuébecCanada
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Li Y, Klápště J, Telfer E, Wilcox P, Graham N, Macdonald L, Dungey HS. Genomic selection for non-key traits in radiata pine when the documented pedigree is corrected using DNA marker information. BMC Genomics 2019; 20:1026. [PMID: 31881838 PMCID: PMC6935163 DOI: 10.1186/s12864-019-6420-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Accepted: 12/22/2019] [Indexed: 12/23/2022] Open
Abstract
Background Non-key traits (NKTs) in radiata pine (Pinus radiata D. Don) refer to traits other than growth, wood density and stiffness, but still of interest to breeders. Branch-cluster frequency, stem straightness, external resin bleeding and internal checking are examples of such traits and are targeted for improvement in radiata pine research programmes. Genomic selection can be conducted before the performance of selection candidates is available so that generation intervals can be reduced. Radiata pine is a species with a long generation interval, which if reduced could significantly increase genetic gain per unit of time. The aim of this study was to evaluate the accuracy and predictive ability of genomic selection and its efficiency over traditional forward selection in radiata pine for the following NKTs: branch-cluster frequency, stem straightness, internal checking, and external resin bleeding. Results Nine hundred and eighty-eight individuals were genotyped using exome capture genotyping by sequencing (GBS) and 67,168 single nucleotide polymorphisms (SNPs) used to develop genomic estimated breeding values (GEBVs) with genomic best linear unbiased prediction (GBLUP). The documented pedigree was corrected using a subset of 704 SNPs. The percentage of trio parentage confirmed was about 49% and about 50% of parents were re-assigned. The accuracy of GEBVs was 0.55–0.75 when using the documented pedigree and 0.61–0.80 when using the SNP-corrected pedigree. A higher percentage of additive genetic variance was explained and a higher predictive ability was observed when using the SNP-corrected pedigree than using the documented pedigree. With the documented pedigree, genomic selection was similar to traditional forward selection when assuming a generation interval of 17 years, but worse than traditional forward selection when assuming a generation interval of 14 years. After the pedigree was corrected, genomic selection led to 37–115% and 13–77% additional genetic gain over traditional forward selection when generation intervals of 17 years and 14 years were assumed, respectively. Conclusion It was concluded that genomic selection with a pedigree corrected by SNP information was an efficient way of improving non-key traits in radiata pine breeding.
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Affiliation(s)
- Yongjun Li
- Scion (New Zealand Forest Research Institute), Private Bag 3020, Rotorua, 3046, New Zealand. .,Agriclture Victoria, AgriBio Centre, 5 Ring Road, Bundoora, VIC, 3083, Australia.
| | - Jaroslav Klápště
- Scion (New Zealand Forest Research Institute), Private Bag 3020, Rotorua, 3046, New Zealand
| | - Emily Telfer
- Scion (New Zealand Forest Research Institute), Private Bag 3020, Rotorua, 3046, New Zealand
| | - Phillip Wilcox
- University of Otago, 362 Leith Steet, North Dunedin, Dunedin, 9016, New Zealand
| | - Natalie Graham
- Scion (New Zealand Forest Research Institute), Private Bag 3020, Rotorua, 3046, New Zealand
| | - Lucy Macdonald
- Scion (New Zealand Forest Research Institute), Private Bag 3020, Rotorua, 3046, New Zealand
| | - Heidi S Dungey
- Scion (New Zealand Forest Research Institute), Private Bag 3020, Rotorua, 3046, New Zealand
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31
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Li Y, Klápště J, Telfer E, Wilcox P, Graham N, Macdonald L, Dungey HS. Genomic selection for non-key traits in radiata pine when the documented pedigree is corrected using DNA marker information. BMC Genomics 2019; 20:1026. [PMID: 31881838 DOI: 10.1186/s12864-12019-16420-12868] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2019] [Accepted: 12/22/2019] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Non-key traits (NKTs) in radiata pine (Pinus radiata D. Don) refer to traits other than growth, wood density and stiffness, but still of interest to breeders. Branch-cluster frequency, stem straightness, external resin bleeding and internal checking are examples of such traits and are targeted for improvement in radiata pine research programmes. Genomic selection can be conducted before the performance of selection candidates is available so that generation intervals can be reduced. Radiata pine is a species with a long generation interval, which if reduced could significantly increase genetic gain per unit of time. The aim of this study was to evaluate the accuracy and predictive ability of genomic selection and its efficiency over traditional forward selection in radiata pine for the following NKTs: branch-cluster frequency, stem straightness, internal checking, and external resin bleeding. RESULTS Nine hundred and eighty-eight individuals were genotyped using exome capture genotyping by sequencing (GBS) and 67,168 single nucleotide polymorphisms (SNPs) used to develop genomic estimated breeding values (GEBVs) with genomic best linear unbiased prediction (GBLUP). The documented pedigree was corrected using a subset of 704 SNPs. The percentage of trio parentage confirmed was about 49% and about 50% of parents were re-assigned. The accuracy of GEBVs was 0.55-0.75 when using the documented pedigree and 0.61-0.80 when using the SNP-corrected pedigree. A higher percentage of additive genetic variance was explained and a higher predictive ability was observed when using the SNP-corrected pedigree than using the documented pedigree. With the documented pedigree, genomic selection was similar to traditional forward selection when assuming a generation interval of 17 years, but worse than traditional forward selection when assuming a generation interval of 14 years. After the pedigree was corrected, genomic selection led to 37-115% and 13-77% additional genetic gain over traditional forward selection when generation intervals of 17 years and 14 years were assumed, respectively. CONCLUSION It was concluded that genomic selection with a pedigree corrected by SNP information was an efficient way of improving non-key traits in radiata pine breeding.
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Affiliation(s)
- Yongjun Li
- Scion (New Zealand Forest Research Institute), Private Bag 3020, Rotorua, 3046, New Zealand.
- Agriclture Victoria, AgriBio Centre, 5 Ring Road, Bundoora, VIC, 3083, Australia.
| | - Jaroslav Klápště
- Scion (New Zealand Forest Research Institute), Private Bag 3020, Rotorua, 3046, New Zealand
| | - Emily Telfer
- Scion (New Zealand Forest Research Institute), Private Bag 3020, Rotorua, 3046, New Zealand
| | - Phillip Wilcox
- University of Otago, 362 Leith Steet, North Dunedin, Dunedin, 9016, New Zealand
| | - Natalie Graham
- Scion (New Zealand Forest Research Institute), Private Bag 3020, Rotorua, 3046, New Zealand
| | - Lucy Macdonald
- Scion (New Zealand Forest Research Institute), Private Bag 3020, Rotorua, 3046, New Zealand
| | - Heidi S Dungey
- Scion (New Zealand Forest Research Institute), Private Bag 3020, Rotorua, 3046, New Zealand
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Ballesta P, Maldonado C, Pérez-Rodríguez P, Mora F. SNP and Haplotype-Based Genomic Selection of Quantitative Traits in Eucalyptus globulus. PLANTS 2019; 8:plants8090331. [PMID: 31492041 PMCID: PMC6783840 DOI: 10.3390/plants8090331] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 09/02/2019] [Accepted: 09/03/2019] [Indexed: 01/02/2023]
Abstract
Eucalyptus globulus (Labill.) is one of the most important cultivated eucalypts in temperate and subtropical regions and has been successfully subjected to intensive breeding. In this study, Bayesian genomic models that include the effects of haplotype and single nucleotide polymorphisms (SNP) were assessed to predict quantitative traits related to wood quality and tree growth in a 6-year-old breeding population. To this end, the following markers were considered: (a) ~14 K SNP markers (SNP), (b) ~3 K haplotypes (HAP), and (c) haplotypes and SNPs that were not assigned to a haplotype (HAP-SNP). Predictive ability values (PA) were dependent on the genomic prediction models and markers. On average, Bayesian ridge regression (BRR) and Bayes C had the highest PA for the majority of traits. Notably, genomic models that included the haplotype effect (either HAP or HAP-SNP) significantly increased the PA of low-heritability traits. For instance, BRR based on HAP had the highest PA (0.58) for stem straightness. Consistently, the heritability estimates from genomic models were higher than the pedigree-based estimates for these traits. The results provide additional perspectives for the implementation of genomic selection in Eucalyptus breeding programs, which could be especially beneficial for improving traits with low heritability.
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Affiliation(s)
- Paulina Ballesta
- Institute of Biological Sciences, University of Talca, 2 Norte 685, Talca 3460000, Chile.
| | - Carlos Maldonado
- Institute of Biological Sciences, University of Talca, 2 Norte 685, Talca 3460000, Chile.
| | - Paulino Pérez-Rodríguez
- Colegio de Postgraduados, Statistics and Computer Sciences, Montecillos, Edo. de México 56230, Mexico.
| | - Freddy Mora
- Institute of Biological Sciences, University of Talca, 2 Norte 685, Talca 3460000, Chile.
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Non-Destructive Evaluation Techniques and What They Tell Us about Wood Property Variation. FORESTS 2019. [DOI: 10.3390/f10090728] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
To maximize utilization of our forest resources, detailed knowledge of wood property variation and the impacts this has on end-product performance is required at multiple scales (within and among trees, regionally). As many wood properties are difficult and time-consuming to measure our knowledge regarding their variation is often inadequate as is our understanding of their responses to genetic and silvicultural manipulation. The emergence of many non-destructive evaluation (NDE) methodologies offers the potential to greatly enhance our understanding of the forest resource; however, it is critical to recognize that any technique has its limitations and it is important to select the appropriate technique for a given application. In this review, we will discuss the following technologies for assessing wood properties both in the field: acoustics, Pilodyn, Resistograph and Rigidimeter and the lab: computer tomography (CT) scanning, DiscBot, near infrared (NIR) spectroscopy, radial sample acoustics and SilviScan. We will discuss these techniques, explore their utilization, and list applications that best suit each methodology. As an end goal, NDE technologies will help researchers worldwide characterize wood properties, develop accurate models for prediction, and utilize field equipment that can validate the predictions. The continued advancement of NDE technologies will also allow researchers to better understand the impact on wood properties on product performance.
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Genomic Prediction of Additive and Non-additive Effects Using Genetic Markers and Pedigrees. G3-GENES GENOMES GENETICS 2019; 9:2739-2748. [PMID: 31263059 PMCID: PMC6686920 DOI: 10.1534/g3.119.201004] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The genetic merit of individuals can be estimated using models with dense markers and pedigree information. Early genomic models accounted only for additive effects. However, the prediction of non-additive effects is important for different forest breeding systems where the whole genotypic value can be captured through clonal propagation. In this study, we evaluated the integration of marker data with pedigree information, in models that included or ignored non-additive effects. We tested the models Reproducing Kernel Hilbert Spaces (RKHS) and BayesA, with additive and additive-dominance frameworks. Model performance was assessed for the traits tree height, diameter at breast height and rust resistance, measured in 923 pine individuals from a structured population of 71 full-sib families. We have also simulated a population with similar genetic properties and evaluated the performance of models for six simulated traits with distinct genetic architectures. Different cross validation strategies were evaluated, and highest accuracies were achieved using within family cross validation. The inclusion of pedigree information in genomic prediction models did not yield higher accuracies. The different RKHS models resulted in similar predictions accuracies, and RKHS and BayesA generated substantially better predictions than pedigree-only models. The additive-BayesA resulted in higher accuracies than RKHS for rust incidence and in simulated additive-oligogenic traits. For DBH, HT and additive-dominance polygenic traits, the RKHS- based models showed slightly higher accuracies than BayesA. Our results indicate that BayesA performs the best for traits with few genes with major effects, while RKHS based models can best predict genotypic effects for clonal selection of complex traits.
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Cappa EP, de Lima BM, da Silva-Junior OB, Garcia CC, Mansfield SD, Grattapaglia D. Improving genomic prediction of growth and wood traits in Eucalyptus using phenotypes from non-genotyped trees by single-step GBLUP. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 284:9-15. [PMID: 31084883 DOI: 10.1016/j.plantsci.2019.03.017] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 01/14/2019] [Accepted: 03/22/2019] [Indexed: 05/10/2023]
Abstract
Genomic Best Linear Unbiased Prediction (GBLUP) in tree breeding typically only uses information from genotyped trees. However, information from phenotyped but non-genotyped trees can also be highly valuable. The single-step GBLUP approach (ssGBLUP) allows genomic prediction to take into account both genotyped and non-genotyped trees simultaneously in a single evaluation. In this study, we investigated the advantage, in terms of breeding value accuracy and bias, of including phenotypic observation from non-genotyped trees in a standard tree GBLUP evaluation. We compared the efficiency of the conventional pedigree-based (ABLUP), GBLUP and ssGBLUP approaches to evaluate eight growth and wood quality traits in a Eucalyptus hybrid population, genotyped with 33,398 single nucleotide polymorphisms (SNPs) using the EucHIP60k. Theoretical accuracies, predictive ability and bias were calculated by ten-fold cross validation on all traits. The use of additional phenotypic information from non-genotyped trees by means of ssGBLUP provided higher predictive ability (from 37% to 75%) and lower prediction bias (from 21% to 73%) for the genetic component of non-phenotyped but genotyped trees when compared to GBLUP. The increase (decrease) in the prediction accuracy (bias) became stronger as trait heritability decreased. We concluded that ssGBLUP is a promising breeding tool to improve accuracies and bias over classical GBLUP for genomic evaluation in Eucalyptus breeding practice.
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Affiliation(s)
- 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), Argentina.
| | | | | | - Carla C Garcia
- International Paper of Brazil, Rodovia SP 340 KM 171, 13840-970, Mogi Guaçu, SP, Brazil
| | - Shawn D Mansfield
- University of British Columbia, Department of Wood Science, Faculty of Forestry, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada
| | - Dario Grattapaglia
- EMBRAPA Genetic Resources and Biotechnology, EPQB Final W5 Norte, 70770-917, Brasilia, DF, Brazil; Genomic Sciences Program, Universidade Católica de Brasília, SGAN 916, Brasilia, DF, Brazil
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Thistlethwaite FR, Ratcliffe B, Klápště J, Porth I, Chen C, Stoehr MU, El-Kassaby YA. Genomic selection of juvenile height across a single-generational gap in Douglas-fir. Heredity (Edinb) 2019; 122:848-863. [PMID: 30631145 PMCID: PMC6781123 DOI: 10.1038/s41437-018-0172-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 11/24/2018] [Accepted: 11/26/2018] [Indexed: 11/30/2022] Open
Abstract
Here, we perform cross-generational GS analysis on coastal Douglas-fir (Pseudotsuga menziesii), reflecting trans-generational selective breeding application. A total of 1321 trees, representing 37 full-sib F1 families from 3 environments in British Columbia, Canada, were used as the training population for (1) EBVs (estimated breeding values) of juvenile height (HTJ) in the F1 generation predicting genomic EBVs of HTJ of 136 individuals in the F2 generation, (2) deregressed EBVs of F1 HTJ predicting deregressed genomic EBVs of F2 HTJ, (3) F1 mature height (HT35) predicting HTJ EBVs in F2, and (4) deregressed F1 HT35 predicting genomic deregressed HTJ EBVs in F2. Ridge regression best linear unbiased predictor (RR-BLUP), generalized ridge regression (GRR), and Bayes-B GS methods were used and compared to pedigree-based (ABLUP) predictions. GS accuracies for scenarios 1 (0.92, 0.91, and 0.91) and 3 (0.57, 0.56, and 0.58) were similar to their ABLUP counterparts (0.92 and 0.60, respectively) (using RR-BLUP, GRR, and Bayes-B). Results using deregressed values fell dramatically for both scenarios 2 and 4 which approached zero in many cases. Cross-generational GS validation of juvenile height in Douglas-fir produced predictive accuracies almost as high as that of ABLUP. Without capturing LD, GS cannot surpass the prediction of ABLUP. Here we tracked pedigree relatedness between training and validation sets. More markers or improved distribution of markers are required to capture LD in Douglas-fir. This is essential for accurate forward selection among siblings as markers that track pedigree are of little use for forward selection of individuals within controlled pollinated families.
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Affiliation(s)
- Frances R Thistlethwaite
- Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada
| | - Blaise Ratcliffe
- Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada
| | - Jaroslav Klápště
- Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Whakarewarewa, Rotorua, 3046, New Zealand
- Department of Genetics and Physiology of Forest Trees, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Praha 6, 165 21, Czech Republic
| | - Ilga Porth
- Département des sciences du bois et de la forêt, Université Laval, G1V 0A6, Québec, QC, Canada
| | - Charles Chen
- Department of Biochemistry and Molecular Biology, Oklahoma State University, Stillwater, OK, 74078-3035, USA
| | - Michael U Stoehr
- British Columbia Ministry of Forests, Lands and Natural Resource Operations, Victoria, BC, V8W 9C2, Canada
| | - Yousry A El-Kassaby
- Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia, 2424 Main Mall, Vancouver, BC, V6T 1Z4, Canada.
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Gienapp P, Calus MPL, Laine VN, Visser ME. Genomic selection on breeding time in a wild bird population. Evol Lett 2019; 3:142-151. [PMID: 31289689 PMCID: PMC6591552 DOI: 10.1002/evl3.103] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Accepted: 01/30/2019] [Indexed: 12/18/2022] Open
Abstract
Artificial selection experiments are a powerful tool in evolutionary biology. Selecting individuals based on multimarker genotypes (genomic selection) has several advantages over phenotype-based selection but has, so far, seen very limited use outside animal and plant breeding. Genomic selection depends on the markers tagging the causal loci that underlie the selected trait. Because the number of necessary markers depends, among other factors, on effective population size, genomic selection may be in practice not feasible in wild populations as most wild populations have much higher effective population sizes than domesticated populations. However, the current possibilities of cost-effective high-throughput genotyping could overcome this limitation and thereby make it possible to apply genomic selection also in wild populations. Using a unique dataset of about 2000 wild great tits (Parus major), a small passerine bird, genotyped on a 650 k SNP chip we calculated genomic breeding values for egg-laying date using the so-called GBLUP approach. In this approach, the pedigree-based relatedness matrix of an "animal model," a special form of the mixed model, is replaced by a marker-based relatedness matrix. Using the marker-based relatedness matrix, the model seemed better able to disentangle genetic and permanent environmental effects. We calculated the accuracy of genomic breeding values by correlating them to the phenotypes of individuals whose phenotypes were excluded from the analysis when estimating the genomic breeding values. The obtained accuracy was about 0.20, with very little effect of the used genomic relatedness estimator but a strong effect of the number of SNPs. The obtained accuracy is lower than typically seen in domesticated species but considerable for a trait with low heritability (∼0.2) as avian breeding time. Our results show that genomic selection is possible also in wild populations with potentially many applications, which we discuss here.
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Affiliation(s)
- Phillip Gienapp
- Department of Animal EcologyNetherlands Institute of Ecology (NIOO‐KNAW)WageningenThe Netherlands
| | - Mario P. L. Calus
- Animal Breeding and GenomicsWageningen University & ResearchWageningenThe Netherlands
| | - Veronika N. Laine
- Department of Animal EcologyNetherlands Institute of Ecology (NIOO‐KNAW)WageningenThe Netherlands
| | - Marcel E. Visser
- Department of Animal EcologyNetherlands Institute of Ecology (NIOO‐KNAW)WageningenThe Netherlands
- Animal Breeding and GenomicsWageningen University & ResearchWageningenThe Netherlands
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Egertsdotter U, Ahmad I, Clapham D. Automation and Scale Up of Somatic Embryogenesis for Commercial Plant Production, With Emphasis on Conifers. FRONTIERS IN PLANT SCIENCE 2019; 10:109. [PMID: 30833951 PMCID: PMC6388443 DOI: 10.3389/fpls.2019.00109] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Accepted: 01/23/2019] [Indexed: 05/19/2023]
Abstract
For large scale production of clonal plants, somatic embryogenesis (SE) has many advantages over other clonal propagation methods such as the rooting of cuttings. In particular, the SE process is more suited to scale up and automation, thereby reducing labor costs and increasing the reliability of the production process. Furthermore, the plants resulting from SE closely resemble those from seeds, as somatic embryos, like zygotic (seed) embryos, develop with good connection between root and shoot, and without the plagiotropism often associated with propagation by cuttings. For practical purposes in breeding programs and for deployment of elite clones, it is valuable that a virtually unlimited number of SE plants can be generated from one original seed embryo; and SE cultures (clones) can be cryostored for at least 20 years, allowing long-term testing of clones. To date, there has however been limited use of SE for large-scale plant production mainly because without automation it is labor-intensive. Development of automation is particularly attractive in countries with high labor costs, where conifer forestry is often of great economic importance. Various approaches for automating SE processes are under investigation and the progress is reviewed here, with emphasis on conifers. These approaches include simplification of culture routines with preference for liquid rather than solid cultures, use of robotics and automation for the harvest of selected individual mature embryos, followed by automated handling of germination and subsequent planting. Different approaches to handle the processes of somatic embryogenesis in conifers are outlined below, followed by an update on efforts to automate the different steps, which are nearing an operational stage.
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Affiliation(s)
- Ulrika Egertsdotter
- Department of Forest Genetics and Plant Physiology, Umeå Plant Science Centre, Swedish University of Agricultural Sciences, Umeå, Sweden
- G.W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- *Correspondence: Ulrika Egertsdotter
| | - Iftikhar Ahmad
- Department of Forest Genetics and Plant Physiology, Umeå Plant Science Centre, Swedish University of Agricultural Sciences, Umeå, Sweden
| | - David Clapham
- Department of Plant Biology, Uppsala BioCenter, Swedish University of Agricultural Sciences, Uppsala, Sweden
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Genomic Prediction of Growth and Stem Quality Traits in Eucalyptus globulus Labill. at Its Southernmost Distribution Limit in Chile. FORESTS 2018. [DOI: 10.3390/f9120779] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The present study was undertaken to examine the ability of different genomic selection (GS) models to predict growth traits (diameter at breast height, tree height and wood volume), stem straightness and branching quality of Eucalyptus globulus Labill. trees using a genome-wide Single Nucleotide Polymorphism (SNP) chip (60 K), in one of the southernmost progeny trials of the species, close to its southern distribution limit in Chile. The GS methods examined were Ridge Regression-BLUP (RRBLUP), Bayes-A, Bayes-B, Bayesian least absolute shrinkage and selection operator (BLASSO), principal component regression (PCR), supervised PCR and a variant of the RRBLUP method that involves the previous selection of predictor variables (RRBLUP-B). RRBLUP-B and supervised PCR models presented the greatest predictive ability (PA), followed by the PCR method, for most of the traits studied. The highest PA was obtained for the branching quality (~0.7). For the growth traits, the maximum values of PA varied from 0.43 to 0.54, while for stem straightness, the maximum value of PA reached 0.62 (supervised PCR). The study population presented a more extended linkage disequilibrium (LD) than other populations of E. globulus previously studied. The genome-wide LD decayed rapidly within 0.76 Mbp (threshold value of r2 = 0.1). The average LD on all chromosomes was r2 = 0.09. In addition, the 0.15% of total pairs of linked SNPs were in a complete LD (r2 = 1), and the 3% had an r2 value >0.5. Genomic prediction, which is based on the reduction in dimensionality and variable selection may be a promising method, considering the early growth of the trees and the low-to-moderate values of heritability found in the traits evaluated. These findings provide new understanding of how develop novel breeding strategies for tree improvement of E. globulus at its southernmost range limit in Chile, which could represent new opportunities for forest planting that can benefit the local economy.
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Chen ZQ, Baison J, Pan J, Karlsson B, Andersson B, Westin J, García-Gil MR, Wu HX. Accuracy of genomic selection for growth and wood quality traits in two control-pollinated progeny trials using exome capture as the genotyping platform in Norway spruce. BMC Genomics 2018; 19:946. [PMID: 30563448 DOI: 10.1186/s12864-12018-15256-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 11/16/2018] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Genomic selection (GS) can increase genetic gain by reducing the length of breeding cycle in forest trees. Here we genotyped 1370 control-pollinated progeny trees from 128 full-sib families in Norway spruce (Picea abies (L.) Karst.), using exome capture as genotyping platform. We used 116,765 high-quality SNPs to develop genomic prediction models for tree height and wood quality traits. We assessed the impact of different genomic prediction methods, genotype-by-environment interaction (G × E), genetic composition, size of the training and validation set, relatedness, and number of SNPs on accuracy and predictive ability (PA) of GS. RESULTS Using G matrix slightly altered heritability estimates relative to pedigree-based method. GS accuracies were about 11-14% lower than those based on pedigree-based selection. The efficiency of GS per year varied from 1.71 to 1.78, compared to that of the pedigree-based model if breeding cycle length was halved using GS. Height GS accuracy decreased to more than 30% while using one site as training for GS prediction and using this model to predict the second site, indicating that G × E for tree height should be accommodated in model fitting. Using a half-sib family structure instead of full-sib structure led to a significant reduction in GS accuracy and PA. The full-sib family structure needed only 750 markers to reach similar accuracy and PA, as compared to 100,000 markers required for the half-sib family, indicating that maintaining the high relatedness in the model improves accuracy and PA. Using 4000-8000 markers in full-sib family structure was sufficient to obtain GS model accuracy and PA for tree height and wood quality traits, almost equivalent to that obtained with all markers. CONCLUSIONS The study indicates that GS would be efficient in reducing generation time of breeding cycle in conifer tree breeding program that requires long-term progeny testing. The sufficient number of trees within-family (16 for growth and 12 for wood quality traits) and number of SNPs (8000) are required for GS with full-sib family relationship. GS methods had little impact on GS efficiency for growth and wood quality traits. GS model should incorporate G × E effect when a strong G × E is detected.
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Affiliation(s)
- Zhi-Qiang Chen
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, SE-90183, Umeå, Sweden
| | - John Baison
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, SE-90183, Umeå, Sweden
| | - Jin Pan
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, SE-90183, Umeå, Sweden
| | - Bo Karlsson
- Skogforsk, Ekebo 2250, SE-268 90, Svalöv, Sweden
| | | | | | - María Rosario García-Gil
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, SE-90183, Umeå, Sweden
| | - Harry X Wu
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, SE-90183, Umeå, Sweden.
- CSIRO NRCA, Black Mountain Laboratory, Canberra, ACT, 2601, Australia.
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Chen ZQ, Baison J, Pan J, Karlsson B, Andersson B, Westin J, García-Gil MR, Wu HX. Accuracy of genomic selection for growth and wood quality traits in two control-pollinated progeny trials using exome capture as the genotyping platform in Norway spruce. BMC Genomics 2018; 19:946. [PMID: 30563448 PMCID: PMC6299659 DOI: 10.1186/s12864-018-5256-y] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 11/16/2018] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Genomic selection (GS) can increase genetic gain by reducing the length of breeding cycle in forest trees. Here we genotyped 1370 control-pollinated progeny trees from 128 full-sib families in Norway spruce (Picea abies (L.) Karst.), using exome capture as genotyping platform. We used 116,765 high-quality SNPs to develop genomic prediction models for tree height and wood quality traits. We assessed the impact of different genomic prediction methods, genotype-by-environment interaction (G × E), genetic composition, size of the training and validation set, relatedness, and number of SNPs on accuracy and predictive ability (PA) of GS. RESULTS Using G matrix slightly altered heritability estimates relative to pedigree-based method. GS accuracies were about 11-14% lower than those based on pedigree-based selection. The efficiency of GS per year varied from 1.71 to 1.78, compared to that of the pedigree-based model if breeding cycle length was halved using GS. Height GS accuracy decreased to more than 30% while using one site as training for GS prediction and using this model to predict the second site, indicating that G × E for tree height should be accommodated in model fitting. Using a half-sib family structure instead of full-sib structure led to a significant reduction in GS accuracy and PA. The full-sib family structure needed only 750 markers to reach similar accuracy and PA, as compared to 100,000 markers required for the half-sib family, indicating that maintaining the high relatedness in the model improves accuracy and PA. Using 4000-8000 markers in full-sib family structure was sufficient to obtain GS model accuracy and PA for tree height and wood quality traits, almost equivalent to that obtained with all markers. CONCLUSIONS The study indicates that GS would be efficient in reducing generation time of breeding cycle in conifer tree breeding program that requires long-term progeny testing. The sufficient number of trees within-family (16 for growth and 12 for wood quality traits) and number of SNPs (8000) are required for GS with full-sib family relationship. GS methods had little impact on GS efficiency for growth and wood quality traits. GS model should incorporate G × E effect when a strong G × E is detected.
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Affiliation(s)
- Zhi-Qiang Chen
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, SE-90183 Umeå, Sweden
| | - John Baison
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, SE-90183 Umeå, Sweden
| | - Jin Pan
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, SE-90183 Umeå, Sweden
| | - Bo Karlsson
- Skogforsk, Ekebo 2250, SE-268 90 Svalöv, Sweden
| | | | | | - María Rosario García-Gil
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, SE-90183 Umeå, Sweden
| | - Harry X. Wu
- Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, SE-90183 Umeå, Sweden
- CSIRO NRCA, Black Mountain Laboratory, Canberra, ACT 2601 Australia
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Azaiez A, Pavy N, Gérardi S, Laroche J, Boyle B, Gagnon F, Mottet MJ, Beaulieu J, Bousquet J. A catalog of annotated high-confidence SNPs from exome capture and sequencing reveals highly polymorphic genes in Norway spruce (Picea abies). BMC Genomics 2018; 19:942. [PMID: 30558528 PMCID: PMC6296092 DOI: 10.1186/s12864-018-5247-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Accepted: 11/14/2018] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Norway spruce [Picea abies (L.) Karst.] is ecologically and economically one of the most important conifer worldwide. Our main goal was to develop a large catalog of annotated high confidence gene SNPs that should sustain the development of genomic tools for the conservation of natural and domesticated genetic diversity resources, and hasten tree breeding efforts in this species. RESULTS Targeted sequencing was achieved by capturing P. abies exome with probes previously designed from the sequenced transcriptome of white spruce (Picea glauca (Moench) Voss). Capture efficiency was high (74.5%) given a high level of exome conservation between the two species. Using stringent criteria, we delimited a set of 61,771 high-confidence SNPs across 13,543 genes. To validate SNPs, a high-throughput genotyping array was developed for a subset of 5571 predicted SNPs representing as many different gene loci, and was used to genotype over 1000 trees. The estimated true positive rate of the resource was 84.2%, which was comparable with the genotyping success rate obtained for P. abies control SNPs recycled from previous genotyping efforts. We also analyzed SNP abundance across various gene functional categories. Several GO terms and gene families involved in stress response were found over-represented in highly polymorphic genes. CONCLUSION The annotated high-confidence SNP catalog developed herein represents a valuable genomic resource, being representative of over 13 K genes distributed across the P. abies genome. This resource should serve a variety of population genomics and breeding applications in Norway spruce.
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Affiliation(s)
- Aïda Azaiez
- Canada Research Chair in Forest Genomics, Forest Research Centre, Université Laval, Québec, Québec G1V 0A6 Canada
- Institute of Integrative Biology and Systems, Université Laval, Québec, Québec G1V 0A6 Canada
| | - Nathalie Pavy
- Canada Research Chair in Forest Genomics, Forest Research Centre, Université Laval, Québec, Québec G1V 0A6 Canada
- Institute of Integrative Biology and Systems, Université Laval, Québec, Québec G1V 0A6 Canada
| | - Sébastien Gérardi
- Canada Research Chair in Forest Genomics, Forest Research Centre, Université Laval, Québec, Québec G1V 0A6 Canada
- Institute of Integrative Biology and Systems, Université Laval, Québec, Québec G1V 0A6 Canada
| | - Jérôme Laroche
- Institute of Integrative Biology and Systems, Université Laval, Québec, Québec G1V 0A6 Canada
| | - Brian Boyle
- Institute of Integrative Biology and Systems, Université Laval, Québec, Québec G1V 0A6 Canada
| | - France Gagnon
- Canada Research Chair in Forest Genomics, Forest Research Centre, Université Laval, Québec, Québec G1V 0A6 Canada
- Institute of Integrative Biology and Systems, Université Laval, Québec, Québec G1V 0A6 Canada
| | - Marie-Josée Mottet
- Direction de la recherche forestière, Ministère des Forêts, de la Faune et des Parcs du Québec, 2700 Einstein, Québec, Québec G1P 3W8 Canada
| | - Jean Beaulieu
- Canada Research Chair in Forest Genomics, Forest Research Centre, Université Laval, Québec, Québec G1V 0A6 Canada
- Institute of Integrative Biology and Systems, Université Laval, Québec, Québec G1V 0A6 Canada
| | - Jean Bousquet
- Canada Research Chair in Forest Genomics, Forest Research Centre, Université Laval, Québec, Québec G1V 0A6 Canada
- Institute of Integrative Biology and Systems, Université Laval, Québec, Québec G1V 0A6 Canada
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Li Y, Dungey HS. Expected benefit of genomic selection over forward selection in conifer breeding and deployment. PLoS One 2018; 13:e0208232. [PMID: 30532178 PMCID: PMC6287808 DOI: 10.1371/journal.pone.0208232] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 11/14/2018] [Indexed: 12/22/2022] Open
Abstract
Genomic selection is a proven technology in animal and plant breeding to accelerate genetic gain, but as yet is to be fully realised in forest tree breeding. This paper examines, through stochastic simulation, the potential benefits of genomic selection (GS) over forward selection (FS) in a typical conifer breeding program. Methods of speeding the deployment of selected material were also considered, including top-grafting onto mature seed orchard ortets, using additional replicates of clones in archives for crossing, and embryogenesis and clonal propagation. Genetic gain per generation was found to increase considerably when the size of the training population was larger (800 c.f. 3000 clones), or when the heritability was higher (0.2 c.f. 0.5). The largest genetic gain, of 24% was achieved where large training populations (3000 clones) and high heritability traits (0.5) were combined. The accuracy of genomic breeding values (GEBVs) increased with the increase in the number of clones in the training population, the heritability of the trait and the density of the SNP markers. Calculated accuracies of simulated GEBVs and genetic gain per unit of time suggested that 2000 clones in the training population is the minimum size for effective genomic selection for conifers. Compared with forward selection, genomic selection with 2000 clones in the training population, and a 60K SNP panel, an increase of 1.58 mm per year in diameter-at-breast-height (DBH) and 2.44 kg/m3 per year for wood density can be expected. After one generation (9-years), this would be equivalent to 14.23 mm and 21.97 kg/m3 for DBH and wood density respectively. Deploying clones of the selected individuals always resulted in higher additional genetic gain than deploying progeny/seedlings. Deploying genetic material selected from genomic selection with top-grafting for early coning appeared to be the best option. Application of genomic selection to conifer breeding programs, combined with deployment tools such as top-grafting and embryogenesis are powerful tools to speed the delivery of genetic gain to the forest.
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Affiliation(s)
- Yongjun Li
- Scion (New Zealand Forest Research Institute), Rotorua, New Zealand
- Agriculture Victoria, AgriBio Centre, DEDJTR, Bundoora, Victoria, Australia
- * E-mail:
| | - Heidi S. Dungey
- Scion (New Zealand Forest Research Institute), Rotorua, New Zealand
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Grattapaglia D, Silva-Junior OB, Resende RT, Cappa EP, Müller BSF, Tan B, Isik F, Ratcliffe B, El-Kassaby YA. Quantitative Genetics and Genomics Converge to Accelerate Forest Tree Breeding. FRONTIERS IN PLANT SCIENCE 2018; 9:1693. [PMID: 30524463 PMCID: PMC6262028 DOI: 10.3389/fpls.2018.01693] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 10/31/2018] [Indexed: 05/18/2023]
Abstract
Forest tree breeding has been successful at delivering genetically improved material for multiple traits based on recurrent cycles of selection, mating, and testing. However, long breeding cycles, late flowering, variable juvenile-mature correlations, emerging pests and diseases, climate, and market changes, all pose formidable challenges. Genetic dissection approaches such as quantitative trait mapping and association genetics have been fruitless to effectively drive operational marker-assisted selection (MAS) in forest trees, largely because of the complex multifactorial inheritance of most, if not all traits of interest. The convergence of high-throughput genomics and quantitative genetics has established two new paradigms that are changing contemporary tree breeding dogmas. Genomic selection (GS) uses large number of genome-wide markers to predict complex phenotypes. It has the potential to accelerate breeding cycles, increase selection intensity and improve the accuracy of breeding values. Realized genomic relationships matrices, on the other hand, provide innovations in genetic parameters' estimation and breeding approaches by tracking the variation arising from random Mendelian segregation in pedigrees. In light of a recent flow of promising experimental results, here we briefly review the main concepts, analytical tools and remaining challenges that currently underlie the application of genomics data to tree breeding. With easy and cost-effective genotyping, we are now at the brink of extensive adoption of GS in tree breeding. Areas for future GS research include optimizing strategies for updating prediction models, adding validated functional genomics data to improve prediction accuracy, and integrating genomic and multi-environment data for forecasting the performance of genetic material in untested sites or under changing climate scenarios. The buildup of phenotypic and genome-wide data across large-scale breeding populations and advances in computational prediction of discrete genomic features should also provide opportunities to enhance the application of genomics to tree breeding.
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Affiliation(s)
- Dario Grattapaglia
- EMBRAPA Recursos Genéticos e BiotecnologiaBrasília, Brazil
- Programa de Ciências Genômicas e BiotecnologiaUniversidade Católica de Brasília, Brasília, Brazil
- Departamento de Biologia CelularUniversidade de Brasília, Brasília, Brazil
- Department of Forestry and Environmental Resources, North Carolina State UniversityRaleigh, NC, United States
| | - Orzenil B. Silva-Junior
- EMBRAPA Recursos Genéticos e BiotecnologiaBrasília, Brazil
- Programa de Ciências Genômicas e BiotecnologiaUniversidade Católica de Brasília, Brasília, Brazil
| | | | - Eduardo P. Cappa
- Centro de Investigación de Recursos Naturales, Instituto de Recursos BiológicosINTA, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y TécnicasBuenos Aires, Argentina
| | - Bárbara S. F. Müller
- EMBRAPA Recursos Genéticos e BiotecnologiaBrasília, Brazil
- Departamento de Biologia CelularUniversidade de Brasília, Brasília, Brazil
| | - Biyue Tan
- Biomaterials DivisionStora Enso AB, Stockholm, Sweden
| | - Fikret Isik
- Department of Forestry and Environmental Resources, North Carolina State UniversityRaleigh, NC, United States
| | - Blaise Ratcliffe
- Department of Forest and Conservation Sciences, Faculty of Forestry, University of British ColumbiaVancouver, BC, Canada
| | - Yousry A. El-Kassaby
- Department of Forest and Conservation Sciences, Faculty of Forestry, University of British ColumbiaVancouver, BC, Canada
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Klápště J, Suontama M, Dungey HS, Telfer EJ, Graham NJ, Low CB, Stovold GT. Effect of Hidden Relatedness on Single-Step Genetic Evaluation in an Advanced Open-Pollinated Breeding Program. J Hered 2018; 109:802-810. [PMID: 30285150 PMCID: PMC6208454 DOI: 10.1093/jhered/esy051] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Accepted: 09/27/2018] [Indexed: 01/17/2023] Open
Abstract
Open-pollinated (OP) mating is frequently used in forest tree breeding due to the relative temporal and financial efficiency of the approach. The trade-off is the lower precision of the estimated genetic parameters. Pedigree/sib-ship reconstruction has been proven as a tool to correct and complete pedigree information and to improve the precision of genetic parameter estimates. Our study analyzed an advanced generation Eucalyptus population from an OP breeding program using single-step genetic evaluation. The relationship matrix inferred from sib-ship reconstruction was used to rescale the marker-based relationship matrix (G matrix). This was compared with a second scenario that used rescaling based on the documented pedigree. The proposed single-step model performed better with respect to both model fit and the theoretical accuracy of breeding values. We found that the prediction accuracy was superior when using the pedigree information only when compared with using a combination of the pedigree and genomic information. This pattern appeared to be mainly a result of accumulated unrecognized relatedness over several breeding cycles, resulting in breeding values being shrunk toward the population mean. Using biased, pedigree-based breeding values as the base with which to correlate predicted GEBVs, resulted in the underestimation of prediction accuracies. Using breeding values estimated on the basis of sib-ship reconstruction resulted in increased prediction accuracies of the genotyped individuals. Therefore, selection of the correct base for estimation of prediction accuracy is critical. The beneficial impact of sib-ship reconstruction using G matrix rescaling was profound, especially in traits with inbreeding depression, such as stem diameter.
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Affiliation(s)
- Jaroslav Klápště
- Scion (New Zealand Forest Research Institute Ltd.), Whakarewarewa, Rotorua, New Zealand. Mari Suontama is now at Skogforsk, Umeå, Sävar SE, Sweden
| | - Mari Suontama
- Scion (New Zealand Forest Research Institute Ltd.), Whakarewarewa, Rotorua, New Zealand. Mari Suontama is now at Skogforsk, Umeå, Sävar SE, Sweden
| | - Heidi S Dungey
- Scion (New Zealand Forest Research Institute Ltd.), Whakarewarewa, Rotorua, New Zealand. Mari Suontama is now at Skogforsk, Umeå, Sävar SE, Sweden
| | - Emily J Telfer
- Scion (New Zealand Forest Research Institute Ltd.), Whakarewarewa, Rotorua, New Zealand. Mari Suontama is now at Skogforsk, Umeå, Sävar SE, Sweden
| | - Natalie J Graham
- Scion (New Zealand Forest Research Institute Ltd.), Whakarewarewa, Rotorua, New Zealand. Mari Suontama is now at Skogforsk, Umeå, Sävar SE, Sweden
| | - Charlie B Low
- Scion (New Zealand Forest Research Institute Ltd.), Whakarewarewa, Rotorua, New Zealand. Mari Suontama is now at Skogforsk, Umeå, Sävar SE, Sweden
| | - Grahame T Stovold
- Scion (New Zealand Forest Research Institute Ltd.), Whakarewarewa, Rotorua, New Zealand. Mari Suontama is now at Skogforsk, Umeå, Sävar SE, Sweden
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Lamara M, Parent GJ, Giguère I, Beaulieu J, Bousquet J, MacKay JJ. Association genetics of acetophenone defence against spruce budworm in mature white spruce. BMC PLANT BIOLOGY 2018; 18:231. [PMID: 30309315 PMCID: PMC6182838 DOI: 10.1186/s12870-018-1434-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Accepted: 09/23/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND Outbreaks of spruce budworm (SBW, Choristoneura fumiferana Clem.) cause major recurrent damage in boreal conifers such as white spruce (Picea glauca [Moench] Voss) and large losses of forest biomass in North America. Although defensive phenolic compounds have recently been linked to chemical resistance against SBW, their genetic basis remains poorly understood in forest trees, especially in conifers. Here, we used diverse association genetics approaches to discover genes and their variants that may control the accumulation of acetophenones, and dissect the genetic architecture of these defence compounds against SBW in white spruce mature trees. RESULTS Out of 4747 single nucleotide polymorphisms (SNPs) from 2312 genes genotyped in a population of 211 unrelated individuals, genetic association analyses identified 35 SNPs in 33 different genes that were significantly associated with the defence traits by using single-locus, multi-locus and multi-trait approaches. The multi-locus approach was particularly effective at detecting SNP-trait associations that explained a large fraction of the phenotypic variance (from 20 to 43%). Significant genes were regulatory including the NAC transcription factor, or they were involved in carbohydrate metabolism, falling into the binding, catalytic or transporter activity functional classes. Most of them were highly expressed in foliage. Weak positive phenotypic correlations were observed between defence and growth traits, indicating little or no evidence of defence-growth trade-offs. CONCLUSIONS This study provides new insights on the genetic architecture of tree defence traits, contributing to our understanding of the physiology of resistance mechanisms to biotic factors and providing a basis for the genetic improvement of the constitutive defence of white spruce against SBW.
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Affiliation(s)
- Mebarek Lamara
- Forest Research Centre and Institute for Systems and Integrative Biology, Département des sciences du bois et de la forêt, Université Laval, Qc, Québec, G1V 0A6 Canada
- Canada Research Chair in Forest Genomics, Université Laval, Qc, Québec, G1V 0A6 Canada
| | | | - Isabelle Giguère
- Forest Research Centre and Institute for Systems and Integrative Biology, Département des sciences du bois et de la forêt, Université Laval, Qc, Québec, G1V 0A6 Canada
| | - Jean Beaulieu
- Forest Research Centre and Institute for Systems and Integrative Biology, Département des sciences du bois et de la forêt, Université Laval, Qc, Québec, G1V 0A6 Canada
- Canada Research Chair in Forest Genomics, Université Laval, Qc, Québec, G1V 0A6 Canada
| | - Jean Bousquet
- Forest Research Centre and Institute for Systems and Integrative Biology, Département des sciences du bois et de la forêt, Université Laval, Qc, Québec, G1V 0A6 Canada
- Canada Research Chair in Forest Genomics, Université Laval, Qc, Québec, G1V 0A6 Canada
| | - John J. MacKay
- Forest Research Centre and Institute for Systems and Integrative Biology, Département des sciences du bois et de la forêt, Université Laval, Qc, Québec, G1V 0A6 Canada
- Department of Plant Sciences, University of Oxford, Oxford, OX1 3RB UK
- Canada Research Chair in Forest Genomics, Université Laval, Qc, Québec, G1V 0A6 Canada
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47
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Hiraoka Y, Fukatsu E, Mishima K, Hirao T, Teshima KM, Tamura M, Tsubomura M, Iki T, Kurita M, Takahashi M, Watanabe A. Potential of Genome-Wide Studies in Unrelated Plus Trees of a Coniferous Species, Cryptomeria japonica (Japanese Cedar). FRONTIERS IN PLANT SCIENCE 2018; 9:1322. [PMID: 30254658 PMCID: PMC6141754 DOI: 10.3389/fpls.2018.01322] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2018] [Accepted: 08/22/2018] [Indexed: 06/08/2023]
Abstract
A genome-wide association study (GWAS) was conducted on more than 30,000 single nucleotide polymorphisms (SNPs) in unrelated first-generation plus tree genotypes from three populations of Japanese cedar Cryptomeria japonica D. Don with genomic prediction for traits of growth, wood properties and male fecundity. Among the assessed populations, genetic characteristics including the extent of linkage disequilibrium (LD) and genetic structure differed and these differences are considered to be due to differences in genetic background. Through population-independent GWAS, several significant SNPs found close to the regions associated with each of these traits and shared in common across the populations were identified. The accuracies of genomic predictions were dependent on the traits and populations and reflected the genetic architecture of traits and genetic characteristics. Prediction accuracies using SNPs selected based on GWAS results were similar to those using all SNPs for several combinations of traits and populations. We discussed the application of genome-wide studies for C. japonica improvement.
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Affiliation(s)
- Yuichiro Hiraoka
- Forest Tree Breeding Center, Forestry and Forest Products Research Institute, Hitachi, Japan
| | - Eitaro Fukatsu
- Kyushu Regional Breeding Office, Forest Tree Breeding Center, Forestry and Forest Products Research Institute, Kumamoto, Japan
| | - Kentaro Mishima
- Forest Tree Breeding Center, Forestry and Forest Products Research Institute, Hitachi, Japan
| | - Tomonori Hirao
- Forest Bio-Research Center, Forestry and Forest Products Research Institute, Hitachi, Japan
| | | | - Miho Tamura
- Faculty of Agriculture, Kyushu University, Fukuoka, Japan
| | - Miyoko Tsubomura
- Forest Tree Breeding Center, Forestry and Forest Products Research Institute, Hitachi, Japan
| | - Taiichi Iki
- Tohoku Regional Breeding Office, Forest Tree Breeding Center, Forestry and Forest Products Research Institute, Takizawa, Japan
| | - Manabu Kurita
- Kyushu Regional Breeding Office, Forest Tree Breeding Center, Forestry and Forest Products Research Institute, Kumamoto, Japan
| | - Makoto Takahashi
- Forest Tree Breeding Center, Forestry and Forest Products Research Institute, Hitachi, Japan
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Accuracy of Genomic Prediction for Foliar Terpene Traits in Eucalyptus polybractea. G3-GENES GENOMES GENETICS 2018; 8:2573-2583. [PMID: 29891736 PMCID: PMC6071609 DOI: 10.1534/g3.118.200443] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Unlike agricultural crops, most forest species have not had millennia of improvement through phenotypic selection, but can contribute energy and material resources and possibly help alleviate climate change. Yield gains similar to those achieved in agricultural crops over millennia could be made in forestry species with the use of genomic methods in a much shorter time frame. Here we compare various methods of genomic prediction for eight traits related to foliar terpene yield in Eucalyptus polybractea, a tree grown predominantly for the production of Eucalyptus oil. The genomic markers used in this study are derived from shallow whole genome sequencing of a population of 480 trees. We compare the traditional pedigree-based additive best linear unbiased predictors (ABLUP), genomic BLUP (GBLUP), BayesB genomic prediction model, and a form of GBLUP based on weighting markers according to their influence on traits (BLUP|GA). Predictive ability is assessed under varying marker densities of 10,000, 100,000 and 500,000 SNPs. Our results show that BayesB and BLUP|GA perform best across the eight traits. Predictive ability was higher for individual terpene traits, such as foliar α-pinene and 1,8-cineole concentration (0.59 and 0.73, respectively), than aggregate traits such as total foliar oil concentration (0.38). This is likely a function of the trait architecture and markers used. BLUP|GA was the best model for the two biomass related traits, height and 1 year change in height (0.25 and 0.19, respectively). Predictive ability increased with marker density for most traits, but with diminishing returns. The results of this study are a solid foundation for yield improvement of essential oil producing eucalypts. New markets such as biopolymers and terpene-derived biofuels could benefit from rapid yield increases in undomesticated oil-producing species.
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Suontama M, Klápště J, Telfer E, Graham N, Stovold T, Low C, McKinley R, Dungey H. Efficiency of genomic prediction across two Eucalyptus nitens seed orchards with different selection histories. Heredity (Edinb) 2018; 122:370-379. [PMID: 29980794 PMCID: PMC6460750 DOI: 10.1038/s41437-018-0119-5] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Revised: 06/11/2018] [Accepted: 06/12/2018] [Indexed: 11/09/2022] Open
Abstract
Genomic selection is expected to enhance the genetic improvement of forest tree species by providing more accurate estimates of breeding values through marker-based relationship matrices compared with pedigree-based methodologies. When adequately robust genomic prediction models are available, an additional increase in genetic gains can be made possible with the shortening of the breeding cycle through elimination of the progeny testing phase and early selection of parental candidates. The potential of genomic selection was investigated in an advanced Eucalyptus nitens breeding population focused on improvement for solid wood production. A high-density SNP chip (EUChip60K) was used to genotype 691 individuals in the breeding population, which represented two seed orchards with different selection histories. Phenotypic records for growth and form traits at age six, and for wood quality traits at age seven were available to build genomic prediction models using GBLUP, which were compared to the traditional pedigree-based alternative using BLUP. GBLUP demonstrated that breeding value accuracy would be improved and substantial increases in genetic gains towards solid wood production would be achieved. Cross-validation within and across two different seed orchards indicated that genomic predictions would likely benefit in terms of higher predictive accuracy from increasing the size of the training data sets through higher relatedness and better utilization of LD.
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Affiliation(s)
- Mari Suontama
- Scion (The New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3046, New Zealand.
| | - Jaroslav Klápště
- Scion (The New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3046, New Zealand
| | - Emily Telfer
- Scion (The New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3046, New Zealand
| | - Natalie Graham
- Scion (The New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3046, New Zealand
| | - Toby Stovold
- Scion (The New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3046, New Zealand
| | - Charlie Low
- Scion (The New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3046, New Zealand
| | - Russell McKinley
- Scion (The New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3046, New Zealand
| | - Heidi Dungey
- Scion (The New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3046, New Zealand
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50
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Nyine M, Uwimana B, Blavet N, Hřibová E, Vanrespaille H, Batte M, Akech V, Brown A, Lorenzen J, Swennen R, Doležel J. Genomic Prediction in a Multiploid Crop: Genotype by Environment Interaction and Allele Dosage Effects on Predictive Ability in Banana. THE PLANT GENOME 2018; 11:170090. [PMID: 30025016 DOI: 10.3835/plantgenome2017.10.0090] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Improving the efficiency of selection in conventional crossbreeding is a major priority in banana ( spp.) breeding. Routine application of classical marker assisted selection (MAS) is lagging in banana due to limitations in MAS tools. Genomic selection (GS) based on genomic prediction models can address some limitations of classical MAS, but the use of GS in banana has not been reported to date. The aim of this study was to evaluate the predictive ability of six genomic prediction models for 15 traits in a multi-ploidy training population. The population consisted of 307 banana genotypes phenotyped under low and high input field management conditions for two crop cycles. The single nucleotide polymorphism (SNP) markers used to fit the models were obtained from genotyping by sequencing (GBS) data. Models that account for additive genetic effects provided better predictions with 12 out of 15 traits. The performance of BayesB model was superior to other models particularly on fruit filling and fruit bunch traits. Models that included averaged environment data were more robust in trait prediction even with a reduced number of markers. Accounting for allele dosage in SNP markers (AD-SNP) reduced predictive ability relative to traditional bi-allelic SNP (BA-SNP), but the prediction trend remained the same across traits. The high predictive values (0.47- 0.75) of fruit filling and fruit bunch traits show the potential of genomic prediction to increase selection efficiency in banana breeding.
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