50 results on '"genomic selection (GS)"'
Search Results
2. Editorial: Plant adaptation to climate change using genomic selection and high-throughput technologies.
- Author
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Ornella, Leonardo Alfredo, Broccanello, Chiara, and Balzarini, Monica
- Subjects
SCIENTIFIC knowledge ,AGRICULTURE ,POPULATION genetics ,SUSTAINABLE agriculture ,LOCUS (Genetics) ,EUCALYPTUS ,BIOFORTIFICATION ,SEED yield - Abstract
This article discusses the use of genomic selection and high-throughput technologies in plant adaptation to climate change. The authors highlight the negative impact of climate change on crop yields and nutritional quality, emphasizing the need for accelerated development of varieties adapted to new conditions. They discuss the use of genomic selection and genome-wide association studies (GWAS) as powerful approaches to investigate marker-trait associations and reduce breeding time and cost. The authors also mention the potential of omics data and machine learning in improving genomic prediction and discovering genes and pathways responsible for agronomic phenotypes. The article presents case studies on drought tolerance in potatoes, biofortification of dry beans, genetic analysis of Eucalyptus dunnii, bHLH genes in grass pea, and responses to sheath rot disease in Manchurian wild rice. The authors conclude that integrating genetic, phenotypic, and environmental data is crucial for developing robust, climate-resilient crops and ensuring food security and agricultural sustainability. They emphasize the importance of collaboration, equitable solutions, and adaptability to emerging trends and technologies in addressing the challenges of climate change. [Extracted from the article]
- Published
- 2024
- Full Text
- View/download PDF
3. Advances in Molecular Breeding of Forage Crops: Technologies, Applications and Prospects.
- Author
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Chen, Shuangyan
- Subjects
PLANT breeding ,FORAGE ,SEXUAL cycle ,LOCUS (Genetics) ,SUSTAINABILITY ,GENOME-wide association studies - Abstract
Molecular breeding has revolutionized the improvement of forage crops by offering precise tools to enhance the yield, quality, and environmental resilience. This review provides a comprehensive overview of the current technologies, applications, and future directions in the field of forage crop molecular breeding. Technological advancements in the field, including Quantitative Trait Loci (QTL) mapping, Genome-Wide Association Studies (GWASs), genomic selection (GS), and genome-editing tools such as CRISPR-Cas9, have significantly advanced the identification and incorporation of beneficial traits into forage species. These approaches have dramatically shortened the breeding cycles and increased the efficiency of developing cultivars with improved yield, disease resistance, stress tolerance, and nutritional profiles. The implementation of these technologies has led to notable successes, as demonstrated by case studies on various forage crops, showcasing enhanced forage quality and adaptability to challenging environmental conditions. Furthermore, the integration of high-throughput phenotyping with advanced bioinformatics tools has streamlined the management of large-scale genomic data, facilitating more precise selection and breeding decisions. Looking ahead, this review explores the potential of emerging technologies, such as the application of artificial intelligence in predictive breeding, along with the associated ethical and regulatory considerations. While we stand to gain benefit from these innovations, the future of molecular breeding in forage crops must also confront the challenges posed by climate change and the imperative of sustainable agricultural practices. This review concludes by emphasizing the transformative impact of molecular breeding on the improvement of forage crop and the critical need for ongoing research and collaboration to fully realize its potential. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
4. Genome-wide association study and genomic selection of flax powdery mildew in Xinjiang Province
- Author
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Leilei Zhu, Gongze Li, Dongliang Guo, Xiao Li, Min Xue, Haixia Jiang, Qingcheng Yan, Fang Xie, Xuefei Ning, and Liqiong Xie
- Subjects
flax ,powdery mildew (PM) ,quantitative trait loci (QTL) ,genome-wide association study (GWAS) ,genomic selection (GS) ,Plant culture ,SB1-1110 - Abstract
Flax powdery mildew (PM), caused by Oidium lini, is a globally distributed fungal disease of flax, and seriously impairs its yield and quality. To data, only three resistance genes and a few putative quantitative trait loci (QTL) have been reported for flax PM resistance. To dissect the resistance mechanism against PM and identify resistant genetic regions, based on four years of phenotypic datasets (2017, 2019 to 2021), a genome-wide association study (GWAS) was performed on 200 flax core accessions using 674,074 SNPs and 7 models. A total of 434 unique quantitative trait nucleotides (QTNs) associated with 331 QTL were detected. Sixty-four loci shared in at least two datasets were found to be significant in haplotype analyses, and 20 of these sites were shared by multiple models. Simultaneously, a large-effect locus (qDI 11.2) was detected repeatedly, which was present in the mapping study of flax pasmo resistance loci. Oil flax had more QTL with positive-effect or favorable alleles (PQTL) and showed higher PM resistance than fiber flax, indicating that effects of these QTL were mainly additive. Furthermore, an excellent resistant variety C120 was identified and can be used to promote planting. Based on 331 QTLs identified through GWAS and the statistical model GBLUP, a genomic selection (GS) model related to flax PM resistance was constructed, and the prediction accuracy rate was 0.96. Our results provide valuable insights into the genetic basis of resistance and contribute to the advancement of breeding programs.
- Published
- 2024
- Full Text
- View/download PDF
5. Genomic selection to improve husk tightness based on genomic molecular markers in maize.
- Author
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Yuncan Liu, Man Ao, Ming Lu, Shubo Zheng, Fangbo Zhu, Yanye Ruan, Yixin Guan, Ao Zhang, and Zhenhai Cui
- Subjects
CORN breeding ,GRAIN harvesting ,STATISTICAL models ,WHEAT straw - Abstract
Introduction: The husk tightness (HTI) in maize plays a crucial role in regulating the water content of ears during the maturity stage, thereby influencing the quality of mechanical grain harvesting in China. Genomic selection (GS), which employs molecular markers, offers a promising approach for identifying and selecting inbred lines with the desired HTI trait in maize breeding. However, the effectiveness of GS is contingent upon various factors, including the genetic architecture of breeding populations, sequencing platforms, and statistical models. Methods: An association panel of maize inbred lines was grown across three sites over two years, divided into four subgroups. GS analysis for HTI prediction was performed using marker data from three sequencing platforms and six marker densities with six statistical methods. Results: The findings indicate that a loosely attached husk can aid in the dissipation of water from kernels in temperate maize germplasms across most environments but not nessarily for tropical-origin maize. Considering the balance between GS prediction accuracy and breeding cost, the optimal prediction strategy is the rrBLUP model, the 50K sequencing platform, a 30% proportion of the test population, and a marker density of r2=0.1. Additionally, selecting a specific SS subgroup for sampling the testing set significantly enhances the predictive capacity for husk tightness. Discussion: The determination of the optimal GS prediction strategy for HTI provides an economically feasible reference for the practice of molecular breeding. It also serves as a reference method for GS breeding of other agronomic traits. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
6. QTL Mapping: Strategy, Progress, and Prospects in Flax
- Author
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You, Frank M., Khan, Nadeem, Shazadee, Hamna, Cloutier, Sylvie, Kole, Chittaranjan, Series Editor, You, Frank M., editor, and Fofana, Bourlaye, editor
- Published
- 2023
- Full Text
- View/download PDF
7. Genomics Assisted Breeding Strategy in Flax
- Author
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Khan, Nadeem, Shazadee, Hamna, Cloutier, Sylvie, You, Frank M., Kole, Chittaranjan, Series Editor, You, Frank M., editor, and Fofana, Bourlaye, editor
- Published
- 2023
- Full Text
- View/download PDF
8. Editorial: Accelerating genetic gain for key traits using genome-wide association studies and genomic selection: promising breeding tools for sustainable agriculture
- Author
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Dwijesh Chandra Mishra, Neeraj Budhlakoti, Philomin Juliana, and Sundeep Kumar
- Subjects
GWAS ,genetic gain ,sustainable agriculture ,genomic selection (GS) ,breeding programs ,Genetics ,QH426-470 - Published
- 2023
- Full Text
- View/download PDF
9. Advances in Molecular Breeding of Forage Crops: Technologies, Applications and Prospects
- Author
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Shuangyan Chen
- Subjects
forage crops ,molecular breeding ,quantitative trait loci (QTL) mapping ,genomic selection (GS) ,CRISPR-Cas9 ,high-throughput phenotyping (HTP) ,Agriculture (General) ,S1-972 - Abstract
Molecular breeding has revolutionized the improvement of forage crops by offering precise tools to enhance the yield, quality, and environmental resilience. This review provides a comprehensive overview of the current technologies, applications, and future directions in the field of forage crop molecular breeding. Technological advancements in the field, including Quantitative Trait Loci (QTL) mapping, Genome-Wide Association Studies (GWASs), genomic selection (GS), and genome-editing tools such as CRISPR-Cas9, have significantly advanced the identification and incorporation of beneficial traits into forage species. These approaches have dramatically shortened the breeding cycles and increased the efficiency of developing cultivars with improved yield, disease resistance, stress tolerance, and nutritional profiles. The implementation of these technologies has led to notable successes, as demonstrated by case studies on various forage crops, showcasing enhanced forage quality and adaptability to challenging environmental conditions. Furthermore, the integration of high-throughput phenotyping with advanced bioinformatics tools has streamlined the management of large-scale genomic data, facilitating more precise selection and breeding decisions. Looking ahead, this review explores the potential of emerging technologies, such as the application of artificial intelligence in predictive breeding, along with the associated ethical and regulatory considerations. While we stand to gain benefit from these innovations, the future of molecular breeding in forage crops must also confront the challenges posed by climate change and the imperative of sustainable agricultural practices. This review concludes by emphasizing the transformative impact of molecular breeding on the improvement of forage crop and the critical need for ongoing research and collaboration to fully realize its potential.
- Published
- 2024
- Full Text
- View/download PDF
10. Genomics in Enhancing Crop Productivity Against Stresses
- Author
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Mishra, V. K., Yadav, Deepali, Srivatava, Yuvraj, Prakash, Chandra, Kashyap, Rohit, Rawat, Rahul, Kashyap, Pawan, Ansari, Shamim Akhtar, editor, Ansari, Mohammad Israil, editor, and Husen, Azamal, editor
- Published
- 2022
- Full Text
- View/download PDF
11. Designing Genomic Solutions to Enhance Abiotic Stress Resistance in Flax
- Author
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Khan, Nadeem, You, Frank M., Cloutier, Sylvie, and Kole, Chittaranjan, editor
- Published
- 2022
- Full Text
- View/download PDF
12. Genomic Designing for Genetic Improvement of Biotic Stress Resistance in Flax
- Author
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You, Frank M., Rashid, Khalid Y., Cloutier, Sylvie, and Kole, Chittaranjan, editor
- Published
- 2022
- Full Text
- View/download PDF
13. Increasing genomic prediction accuracy for unphenotyped full-sib families by modeling additive and dominance effects with large datasets in white spruce.
- Author
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Nadeau, Simon, Beaulieu, Jean, Gezan, Salvador A., Perron, Martin, Bousquet, Jean, and Lenz, Patrick R. N.
- Subjects
WHITE spruce ,TREE breeding ,WOOD quality ,PLANT breeding ,SOCIAL dominance ,RESAMPLING (Statistics) - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
14. Alternative scoring methods of fusarium head blight resistance for genomic assisted breeding.
- Author
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Garcia-Abadillo, J., Morales, L., Buerstmayr, H., Michel, S., Lillemo, M., Holzapfel, J., Hartl, L., Akdemir, D., Carvalho, H. F., and Isidro-Sánchez, J.
- Subjects
PLANT breeding ,GENOME-wide association studies ,FUSARIUM ,MYCOSES ,SIGNAL detection ,WHEAT diseases & pests - Abstract
Fusarium head blight (FHB) is a fungal disease of wheat (Triticum aestivum.L) that causes yield losses and produces mycotoxins which could easily exceed the limits of the EU regulations. Resistance to FHB has a complex genetic architecture and accurate evaluation in breeding programs is key to selecting resistant varieties. The Area Under the Disease Progress Curve (AUDPC) is one of the commonly metric used as a standard methodology to score FHB. Although efficient, AUDPC requires significant costs in phenotyping to cover the entire disease development pattern. Here, we show that there are more efficient alternatives to AUDPC (angle, growing degree days to reach 50% FHB severity, and FHB maximum variance) that reduce the number of field assessments required and allow for fair comparisons between unbalanced evaluations across trials. Furthermore, we found that the evaluation method that captures the maximum variance in FHB severity across plots is the most optimal approach for scoring FHB. In addition, results obtained on experimental data were validated on a simulated experiment where the disease progress curve was modeled as a sigmoid curve with known parameters and assessment protocols were fully controlled. Results show that alternative metrics tested in this study captured key components of quantitative plant resistance. Moreover, the new metrics could be a starting point for more accurate methods for measuring FHB in the field. For example, the optimal interval for FHB evaluation could be predicted using prior knowledge from historical weather data and FHB scores from previous trials. Finally, the evaluation methods presented in this study can reduce the FHB phenotyping burden in plant breeding with minimal losses on signal detection, resulting in a response variable available to use in data-driven analysis such as genome-wide association studies or genomic selection. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
15. Nested association mapping population in crops: current status and future prospects.
- Author
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Kitony, Justine Kipruto
- Abstract
The recent advancement of bioinformatics tools and next-generation sequencing (NGS) has created enormous opportunities for a thorough understanding of quantitative (complex) traits in plants. One of the key drivers facilitating the mining of beneficial alleles for climate adaptation and increased crop productivity is multi-parental mapping populations. In this article, the current status and opportunities of multi-parental mapping populations for breeding and genetics are discussed. The paper particularly focuses on nested association mapping (NAM) population. NAM is primarily made up of inbred lines derived from crossing/backcrossing a common parent(s) to multiple diversity donor lines. NAM has high genetic diversity and low population structure, making it ideal for discovering genes and elucidating the genetic architecture of quantitative traits in plants. Because NAM lines are immortal, they allow for repeated measurements and multi-environment testing, which can be used in traditional functional genomics. Overall, the NAM population is a simple but powerful genetic resource that can be used as a gene mapping tool as well as a platform for evaluating models used in plant breeding. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
16. Increasing genomic prediction accuracy for unphenotyped full-sib families by modeling additive and dominance effects with large datasets in white spruce
- Author
-
Simon Nadeau, Jean Beaulieu, Salvador A. Gezan, Martin Perron, Jean Bousquet, and Patrick R. N. Lenz
- Subjects
Genomic selection (GS) ,non-additive genetic effects ,mate allocation ,wood quality traits ,growth traits ,conifers ,Plant culture ,SB1-1110 - Abstract
IntroductionGenomic 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.MethodsUsing 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.ResultsWhile 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.DiscussionThis 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.
- Published
- 2023
- Full Text
- View/download PDF
17. Genomic selection in algae with biphasic lifecycles: A Saccharina latissima (sugar kelp) case study
- Author
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Mao Huang, Kelly R. Robbins, Yaoguang Li, Schery Umanzor, Michael Marty-Rivera, David Bailey, Margaret Aydlett, Jeremy Schmutz, Jane Grimwood, Charles Yarish, Scott Lindell, and Jean-Luc Jannink
- Subjects
sugar kelp (Saccharina latissima) ,genomic selection (GS) ,genotyping ,phenotyping ,brown algae ,biphasic cycle ,Science ,General. Including nature conservation, geographical distribution ,QH1-199.5 - Abstract
IntroductionSugar kelp (Saccharina latissima) has a biphasic life cycle, allowing selection on both thediploid sporophytes (SPs) and haploid gametophytes (GPs).MethodsWe trained a genomic selection (GS) model from farm-tested SP phenotypic data and used a mixed-ploidy additive relationship matrix to predict GP breeding values. Topranked GPs were used to make crosses for further farm evaluation. The relationship matrix included 866 individuals: a) founder SPs sampled from the wild; b) progeny GPs from founders; c) Farm-tested SPs crossed from b); and d) progeny GPs from farm-tested SPs. The complete pedigree-based relationship matrix was estimated for all individuals. A subset of founder SPs (n = 58) and GPs (n = 276) were genotyped with Diversity Array Technology and whole genome sequencing, respectively. We evaluated GS prediction accuracy via cross validation for SPs tested on farm in 2019 and 2020 using a basic GBLUP model. We also estimated the general combining ability (GCA) and specific combining ability (SCA) variances of parental GPs. A total of 11 yield-related and morphology traits were evaluated.ResultsThe cross validation accuracies for dry weight per meter (r ranged from 0.16 to 0.35) and wet weight per meter (r ranged 0.19 to 0.35) were comparable to GS accuracy for yield traits in terrestrial crops. For morphology traits, cross validation accuracy exceeded 0.18 in all scenarios except for blade thickness in the second year. Accuracy in a third validation year (2021) was 0.31 for dry weight per meter over a confirmation set of 87 individuals.DiscussionOur findings indicate that progress can be made in sugar kelp breeding by using genomic selection.
- Published
- 2023
- Full Text
- View/download PDF
18. Alternative scoring methods of fusarium head blight resistance for genomic assisted breeding
- Author
-
J. Garcia-Abadillo, L. Morales, H. Buerstmayr, S. Michel, M. Lillemo, J. Holzapfel, L. Hartl, D. Akdemir, H. F. Carvalho, and J. Isidro-Sánchez
- Subjects
genomic selection (GS) ,fusarium head blight (FHB) ,wheat ,quantitative resistance ,plant breeding ,simulation and empirical evidence ,Plant culture ,SB1-1110 - Abstract
Fusarium head blight (FHB) is a fungal disease of wheat (Triticum aestivum.L) that causes yield losses and produces mycotoxins which could easily exceed the limits of the EU regulations. Resistance to FHB has a complex genetic architecture and accurate evaluation in breeding programs is key to selecting resistant varieties. The Area Under the Disease Progress Curve (AUDPC) is one of the commonly metric used as a standard methodology to score FHB. Although efficient, AUDPC requires significant costs in phenotyping to cover the entire disease development pattern. Here, we show that there are more efficient alternatives to AUDPC (angle, growing degree days to reach 50% FHB severity, and FHB maximum variance) that reduce the number of field assessments required and allow for fair comparisons between unbalanced evaluations across trials. Furthermore, we found that the evaluation method that captures the maximum variance in FHB severity across plots is the most optimal approach for scoring FHB. In addition, results obtained on experimental data were validated on a simulated experiment where the disease progress curve was modeled as a sigmoid curve with known parameters and assessment protocols were fully controlled. Results show that alternative metrics tested in this study captured key components of quantitative plant resistance. Moreover, the new metrics could be a starting point for more accurate methods for measuring FHB in the field. For example, the optimal interval for FHB evaluation could be predicted using prior knowledge from historical weather data and FHB scores from previous trials. Finally, the evaluation methods presented in this study can reduce the FHB phenotyping burden in plant breeding with minimal losses on signal detection, resulting in a response variable available to use in data-driven analysis such as genome-wide association studies or genomic selection.
- Published
- 2023
- Full Text
- View/download PDF
19. Domestication of newly evolved hexaploid wheat--A journey of wild grass to cultivated wheat.
- Author
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Gohar, Sasha, Sajjad, Muhammad, Zulfiqar, Sana, Jiajun Liu, Jiajie Wu, and Mehboob-ur-Rahman
- Subjects
WHEATGRASSES ,MENDEL'S law ,WHEAT ,INSECT pests ,GENETIC variation ,FOOD security ,HERBICIDES - Abstract
Domestication of wheat started with the dawn of human civilization. Since then, improvement in various traits including resistance to diseases, insect pests, saline and drought stresses, grain yield, and quality were improved through selections by early farmers and then planned hybridization after the discovery of Mendel's laws. In the 1950s, genetic variability was created using mutagens followed by the selection of superior mutants. Over the last 3 decades, research was focused on developing superior hybrids, initiating marker-assisted selection and targeted breeding, and developing genetically modified wheat to improve the grain yield, tolerance to drought, salinity, terminal heat and herbicide, and nutritive quality. Acceptability of genetically modified wheat by the end-user remained a major hurdle in releasing into the environment. Since the beginning of the 21st century, changing environmental conditions proved detrimental to achieving sustainability in wheat production particularly in developing countries. It is suggested that high-tech phenotyping assays and genomic procedures together with speed breeding procedures will be instrumental in achieving food security beyond 2050. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
20. Transformative changes in tree breeding for resilient forest restoration
- Author
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Duncan Ray, Mats Berlin, Ricardo Alia, Leopoldo Sanchez, Jari Hynynen, Santiago González-Martinez, and Catherine Bastien
- Subjects
assisted translocation ,natural colonisation ,forest reproductive material (FRM) ,climate portal ,genomic selection (GS) ,genome-wide association studies (GWAS) ,Forestry ,SD1-669.5 ,Environmental sciences ,GE1-350 - Abstract
Deciding how to establish woodland in forest restoration is not straightforward as different outcomes may be obtained from different establishment approaches, each with cost implications and degree of success limitations attached. Planning restoration requires knowledge of site conditions, including how sites are likely to respond under climate change. For objectives of production and high timber quality it is likely that ground preparation will be used, and planting with forest reproductive material (FRM) of known traits, such as: high survival and growth in establishment, drought tolerance adequate for climate projections, good resistance to pests and pathogens. For objectives associated with biodiversity, carbon sequestration, water supply protection, soil protection, natural regeneration could be a less costly solution with a limited amount of assisted translocation of selected FRM to improve resilience. If objectives are for rewilding forest areas, a degree of natural colonisation perhaps with translocation of some FRM could be a solution. Ignoring site conditions and suitability of available sources of FRM for forest restoration is likely to provide unexpected results with a mix of open ground, scrub and scattered trees resulting from climate, herbivore, and browsing impacts. The recent B4EST EU Horizon 2020 project examined progress in novel rapid approaches for testing the quality of FRM from existing genetic trials. Here we review the work of B4EST to show the opportunities from transformative tree breeding in forest restoration schemes, including: new climate projection ensembles at high temporal and spatial resolution to develop norms of reaction and transfer models with genetic components; multi-environment genotype-phenotype associations and multi-locus genotype-environment associations in identifying drivers of local adaptation; techniques for genomic selection using single nucleotide polymorphism (SNP) arrays to derive functional traits from polygenic associations; work on seed orchard site and climate specific FRM and zones for deployment; and work on some of the forest ecosystem service benefits derived at a landscape scale. We conclude that tree-breeding will provide robust forest restoration for planting, and rewilding (assisted natural regeneration), and if not “ignoring” but instead assisting natural colonisation processes – tree breeding may improve long-term forest resilience under environmental change.
- Published
- 2022
- Full Text
- View/download PDF
21. Domestication of newly evolved hexaploid wheat—A journey of wild grass to cultivated wheat
- Author
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Sasha Gohar, Muhammad Sajjad, Sana Zulfiqar, Jiajun Liu, Jiajie Wu, and Mehboob-ur- Rahman
- Subjects
domestication ,hybrid wheat ,NGS ,CRISPR ,genomic selection (GS) ,climate change ,Genetics ,QH426-470 - Abstract
Domestication of wheat started with the dawn of human civilization. Since then, improvement in various traits including resistance to diseases, insect pests, saline and drought stresses, grain yield, and quality were improved through selections by early farmers and then planned hybridization after the discovery of Mendel’s laws. In the 1950s, genetic variability was created using mutagens followed by the selection of superior mutants. Over the last 3 decades, research was focused on developing superior hybrids, initiating marker-assisted selection and targeted breeding, and developing genetically modified wheat to improve the grain yield, tolerance to drought, salinity, terminal heat and herbicide, and nutritive quality. Acceptability of genetically modified wheat by the end-user remained a major hurdle in releasing into the environment. Since the beginning of the 21st century, changing environmental conditions proved detrimental to achieving sustainability in wheat production particularly in developing countries. It is suggested that high-tech phenotyping assays and genomic procedures together with speed breeding procedures will be instrumental in achieving food security beyond 2050.
- Published
- 2022
- Full Text
- View/download PDF
22. Selection strategies to introgress water deficit tolerance derived from Solanum galapagense accession LA1141 into cultivated tomato.
- Author
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Fenstemaker, Sean, Jin Cho, McCoy, Jack E., Mercer, Kristin L., and Francis, David M.
- Subjects
LOCUS (Genetics) ,SINGLE nucleotide polymorphisms ,DEFICIT irrigation ,SOLANUM ,TOMATOES ,THERMOGRAPHY - Abstract
Crop wild relatives have been used as a source of genetic diversity for over one hundred years. The wild tomato relative Solanum galapagense accession LA1141 demonstrates the ability to tolerate deficit irrigation, making it a potential resource for crop improvement. Accessing traits from LA1141 through introgression may improve the response of cultivated tomatoes grown in water-limited environments. Canopy temperature is a proxy for physiological traits which are challenging to measure efficiently and may be related to water deficit tolerance. We optimized phenotypic evaluation based on variance partitioning and further show that objective phenotyping methods coupled with genomic prediction lead to gain under selection for water deficit tolerance. The objectives of this work were to improve phenotyping workflows for measuring canopy temperature, mapping quantitative trait loci (QTLs) from LA1141 that contribute to water deficit tolerance and comparing selection strategies. The phenotypic variance attributed to genetic causes for canopy temperature was higher when estimated from thermal images relative to estimates based on an infrared thermometer. Composite interval mapping using BC
2 S3 families, genotyped with single nucleotide polymorphisms, suggested that accession LA1141 contributed alleles that lower canopy temperature and increase plant turgor under water deficit. QTLs for lower canopy temperature were mapped to chromosomes 1 and 6 and explained between 6.6 and 9.5% of the total phenotypic variance. QTLs for higher leaf turgor were detected on chromosomes 5 and 7 and explained between 6.8 and 9.1% of the variance. We advanced tolerant BC2 S3 families to the BC2 S5 generation using selection indices based on phenotypic values and genomic estimated breeding values (GEBVs). Phenotypic, genomic, and combined selection strategies demonstrated gain under selection and improved performance compared to randomly advanced BC2 S5 progenies. Leaf turgor, canopy temperature, stomatal conductance, and vapor pressure deficit (VPD) were evaluated and compared in BC2 S5 progenies grown under deficit irrigation. Progenies co-selected for phenotypic values and GEBVs wilted less, had significantly lower canopy temperature, higher stomatal conductance, and lower VPD than randomly advanced lines. The fruit size of water deficit tolerant selections was small compared to the recurrent parent. However, lines with acceptable yield, canopy width, and quality parameters were recovered. These results suggest that we can create selection indices to improve water deficit tolerance in a recurrent parent background, and additional crossing and evaluation are warranted. [ABSTRACT FROM AUTHOR]- Published
- 2022
- Full Text
- View/download PDF
23. Applied phenomics and genomics for improving barley yellow dwarf resistance in winter wheat.
- Author
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Silva, Paula, Evers, Byron, Kieffaber, Alexandria, Xu Wang, Brown, Richard, Liangliang Gao, Fritz, Allan, Crain, Jared, and Poland, Jesse
- Subjects
- *
BARLEY , *WINTER wheat , *GENOMICS , *VIRUS diseases , *GRAIN yields , *WHEAT - Abstract
Barley yellow dwarf is one of the major viral diseases of cereals. Phenotyping barley yellow dwarf in wheat is extremely challenging due to similarities to other biotic and abiotic stresses. Breeding for resistance is additionally challenging as the wheat primary germplasm pool lacks genetic resistance, with most of the few resistance genes named to date originating from a wild relative species. The objectives of this study were to (1) evaluate the use of high-throughput phenotyping to improve barley yellow dwarf assessment; (2) identify genomic regions associated with barley yellow dwarf resistance; and (3) evaluate the ability of genomic selection models to predict barley yellow dwarf resistance. Up to 107 wheat lines were phenotyped during each of 5 field seasons under both insecticide treated and untreated plots. Across all seasons, barley yellow dwarf severity was lower within the insecticide treatment along with increased plant height and grain yield compared with untreated entries. Only 9.2% of the lines were positive for the presence of the translocated segment carrying the resistance gene Bdv2. Despite the low frequency, this region was identified through association mapping. Furthermore, we mapped a potentially novel genomic region for barley yellow dwarf resistance on chromosome 5AS. Given the variable heritability of the trait (0.211-0.806), we obtained a predictive ability for barley yellow dwarf severity ranging between 0.06 and 0.26. Including the presence or absence of Bdv2 as a covariate in the genomic selection models had a large effect for predicting barley yellow dwarf but almost no effect for other observed traits. This study was the first attempt to characterize barley yellow dwarf using field-high-throughput phenotyping and apply genomic selection to predict disease severity. These methods have the potential to improve barley yellow dwarf characterization, additionally identifying new sources of resistance will be crucial for delivering barley yellow dwarf resistant germplasm. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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- View/download PDF
24. Selection strategies to introgress water deficit tolerance derived from Solanum galapagense accession LA1141 into cultivated tomato
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Sean Fenstemaker, Jin Cho, Jack E. McCoy, Kristin L. Mercer, and David M. Francis
- Subjects
thermal images ,genomic selection (GS) ,proximal sensing ,high-throughput phenotyping ,inbred backcross method ,canopy temperature (CT) ,Plant culture ,SB1-1110 - Abstract
Crop wild relatives have been used as a source of genetic diversity for over one hundred years. The wild tomato relative Solanum galapagense accession LA1141 demonstrates the ability to tolerate deficit irrigation, making it a potential resource for crop improvement. Accessing traits from LA1141 through introgression may improve the response of cultivated tomatoes grown in water-limited environments. Canopy temperature is a proxy for physiological traits which are challenging to measure efficiently and may be related to water deficit tolerance. We optimized phenotypic evaluation based on variance partitioning and further show that objective phenotyping methods coupled with genomic prediction lead to gain under selection for water deficit tolerance. The objectives of this work were to improve phenotyping workflows for measuring canopy temperature, mapping quantitative trait loci (QTLs) from LA1141 that contribute to water deficit tolerance and comparing selection strategies. The phenotypic variance attributed to genetic causes for canopy temperature was higher when estimated from thermal images relative to estimates based on an infrared thermometer. Composite interval mapping using BC2S3 families, genotyped with single nucleotide polymorphisms, suggested that accession LA1141 contributed alleles that lower canopy temperature and increase plant turgor under water deficit. QTLs for lower canopy temperature were mapped to chromosomes 1 and 6 and explained between 6.6 and 9.5% of the total phenotypic variance. QTLs for higher leaf turgor were detected on chromosomes 5 and 7 and explained between 6.8 and 9.1% of the variance. We advanced tolerant BC2S3 families to the BC2S5 generation using selection indices based on phenotypic values and genomic estimated breeding values (GEBVs). Phenotypic, genomic, and combined selection strategies demonstrated gain under selection and improved performance compared to randomly advanced BC2S5 progenies. Leaf turgor, canopy temperature, stomatal conductance, and vapor pressure deficit (VPD) were evaluated and compared in BC2S5 progenies grown under deficit irrigation. Progenies co-selected for phenotypic values and GEBVs wilted less, had significantly lower canopy temperature, higher stomatal conductance, and lower VPD than randomly advanced lines. The fruit size of water deficit tolerant selections was small compared to the recurrent parent. However, lines with acceptable yield, canopy width, and quality parameters were recovered. These results suggest that we can create selection indices to improve water deficit tolerance in a recurrent parent background, and additional crossing and evaluation are warranted.
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- 2022
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25. Perspectives and recent progress of genome-wide association studies (GWAS) in fruits.
- Author
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Zahid, Ghassan, Aka Kaçar, Yıldız, Dönmez, Dicle, Küden, Ayzin, and Giordani, Tommaso
- Abstract
Background: Earlier next-generation sequencing technologies are being vastly used to explore, administer, and investigate the gene space with accurate profiling of nucleotide variations in the germplasm. Overview and Progress: Recently, novel advancements in high-throughput sequencing technologies allow a genotyping-by-sequencing approach that has opened up new horizons for extensive genotyping exploiting single-nucleotide-polymorphisms (SNPs). This method acts as a bridge to support and minimize a genotype to phenotype gap allowing genetic selection at the genome-wide level, named genomic selection that could facilitate the selection of traits also in the pomology sector. In addition to this, genome-wide genotyping is a prerequisite for genome-wide association studies that have been used successfully to discover the genes, which control polygenic traits including the genetic loci, associated with the trait of interest in fruit crops. Aims and Prospects: This review article emphasizes the role of genome-wide approaches to unlock and explore the genetic potential along with the detection of SNPs affecting the phenotype of fruit crops and highlights the prospects of genome-wide association studies in fruits. [ABSTRACT FROM AUTHOR]
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- 2022
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26. Cotton Breeding in Australia: Meeting the Challenges of the 21st Century.
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Conaty, Warren C., Broughton, Katrina J., Egan, Lucy M., Li, Xiaoqing, Li, Zitong, Liu, Shiming, Llewellyn, Danny J., MacMillan, Colleen P., Moncuquet, Philippe, Rolland, Vivien, Ross, Brett, Sargent, Demi, Zhu, Qian-Hao, Pettolino, Filomena A., and Stiller, Warwick N.
- Subjects
TWENTY-first century ,COTTON ,COTTONSEED ,GENOME editing ,JOINT ventures ,DIETARY fiber - Abstract
The Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program is the sole breeding effort for cotton in Australia, developing high performing cultivars for the local industry which is worth∼AU$3 billion per annum. The program is supported by Cotton Breeding Australia, a Joint Venture between CSIRO and the program's commercial partner, Cotton Seed Distributors Ltd. (CSD). While the Australian industry is the focus, CSIRO cultivars have global impact in North America, South America, and Europe. The program is unique compared with many other public and commercial breeding programs because it focuses on diverse and integrated research with commercial outcomes. It represents the full research pipeline, supporting extensive long-term fundamental molecular research; native and genetically modified (GM) trait development; germplasm enhancement focused on yield and fiber quality improvements; integration of third-party GM traits; all culminating in the release of new commercial cultivars. This review presents evidence of past breeding successes and outlines current breeding efforts, in the areas of yield and fiber quality improvement, as well as the development of germplasm that is resistant to pests, diseases and abiotic stressors. The success of the program is based on the development of superior germplasm largely through field phenotyping, together with strong commercial partnerships with CSD and Bayer CropScience. These relationships assist in having a shared focus and ensuring commercial impact is maintained, while also providing access to markets, traits, and technology. The historical successes, current foci and future requirements of the CSIRO cotton breeding program have been used to develop a framework designed to augment our breeding system for the future. This will focus on utilizing emerging technologies from the genome to phenome, as well as a panomics approach with data management and integration to develop, test and incorporate new technologies into a breeding program. In addition to streamlining the breeding pipeline for increased genetic gain, this technology will increase the speed of trait and marker identification for use in genome editing, genomic selection and molecular assisted breeding, ultimately producing novel germplasm that will meet the coming challenges of the 21st Century. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
27. Editorial: Advances in Breeding for Quantitative Disease Resistance.
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Hoyos-Villegas, Valerio, Chen, Jianjun, Mastrangelo, Anna Maria, and Raman, Harsh
- Subjects
NATURAL immunity ,GENOTYPE-environment interaction ,DURUM wheat ,WINTER wheat ,SOYBEAN cyst nematode ,LOCUS (Genetics) - Abstract
Additionally, quantitative disease loci (QDL) appears to be independent from the presence of qualitative resistance; thus, it is possible that breeding for QDR may not necessarily include genes for qualitative resistance. Genome-wide association, quantitative disease resistance, plant breeding, quantitative trait loci (QTL) analysis, pathosystem, fine mapping, pre-breeding, genomic selection (GS) Keywords: quantitative disease resistance; plant breeding; genome-wide association; quantitative trait loci (QTL) analysis; pathosystem; fine mapping; pre-breeding; genomic selection (GS) EN quantitative disease resistance plant breeding genome-wide association quantitative trait loci (QTL) analysis pathosystem fine mapping pre-breeding genomic selection (GS) 1 5 5 04/18/22 20220413 NES 220413 In plant breeding and genetics, traits are frequently classified into qualitative and quantitative. It is widely recognized that QDR provides long term host defense toward the disease, probably due to multiple genes requiring mutation for resistance breakdown as opposed to single genes as in the case of gene-for-gene resistance. [Extracted from the article]
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- 2022
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28. Editorial: Genomic Selection: Lessons Learned and Perspectives
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Johannes W. R. Martini, Sarah J. Hearne, Brian Gardunia, Valentin Wimmer, and Fernando H. Toledo
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genomic selection (GS) ,plant breeding ,selection gain ,breeding schemes ,genotype-by-environment interaction ,Plant culture ,SB1-1110 - Published
- 2022
- Full Text
- View/download PDF
29. Cotton Breeding in Australia: Meeting the Challenges of the 21st Century
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Warren C. Conaty, Katrina J. Broughton, Lucy M. Egan, Xiaoqing Li, Zitong Li, Shiming Liu, Danny J. Llewellyn, Colleen P. MacMillan, Philippe Moncuquet, Vivien Rolland, Brett Ross, Demi Sargent, Qian-Hao Zhu, Filomena A. Pettolino, and Warwick N. Stiller
- Subjects
cotton ,plant breeding ,genomic selection (GS) ,gene editing ,phenomics ,GM traits ,Plant culture ,SB1-1110 - Abstract
The Commonwealth Scientific and Industrial Research Organisation (CSIRO) cotton breeding program is the sole breeding effort for cotton in Australia, developing high performing cultivars for the local industry which is worth∼AU$3 billion per annum. The program is supported by Cotton Breeding Australia, a Joint Venture between CSIRO and the program’s commercial partner, Cotton Seed Distributors Ltd. (CSD). While the Australian industry is the focus, CSIRO cultivars have global impact in North America, South America, and Europe. The program is unique compared with many other public and commercial breeding programs because it focuses on diverse and integrated research with commercial outcomes. It represents the full research pipeline, supporting extensive long-term fundamental molecular research; native and genetically modified (GM) trait development; germplasm enhancement focused on yield and fiber quality improvements; integration of third-party GM traits; all culminating in the release of new commercial cultivars. This review presents evidence of past breeding successes and outlines current breeding efforts, in the areas of yield and fiber quality improvement, as well as the development of germplasm that is resistant to pests, diseases and abiotic stressors. The success of the program is based on the development of superior germplasm largely through field phenotyping, together with strong commercial partnerships with CSD and Bayer CropScience. These relationships assist in having a shared focus and ensuring commercial impact is maintained, while also providing access to markets, traits, and technology. The historical successes, current foci and future requirements of the CSIRO cotton breeding program have been used to develop a framework designed to augment our breeding system for the future. This will focus on utilizing emerging technologies from the genome to phenome, as well as a panomics approach with data management and integration to develop, test and incorporate new technologies into a breeding program. In addition to streamlining the breeding pipeline for increased genetic gain, this technology will increase the speed of trait and marker identification for use in genome editing, genomic selection and molecular assisted breeding, ultimately producing novel germplasm that will meet the coming challenges of the 21st Century.
- Published
- 2022
- Full Text
- View/download PDF
30. Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection.
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Robert, Pauline, Auzanneau, Jérôme, Goudemand, Ellen, Oury, François-Xavier, Rolland, Bernard, Heumez, Emmanuel, Bouchet, Sophie, Le Gouis, Jacques, and Rincent, Renaud
- Subjects
- *
WHEAT breeding , *GRAIN yields , *PLANT breeding , *FORECASTING , *WHEAT , *NEAR infrared spectroscopy - Abstract
Key message: Phenomic selection is a promising alternative or complement to genomic selection in wheat breeding. Models combining spectra from different environments maximise the predictive ability of grain yield and heading date of wheat breeding lines. Phenomic selection (PS) is a recent breeding approach similar to genomic selection (GS) except that genotyping is replaced by near-infrared (NIR) spectroscopy. PS can potentially account for non-additive effects and has the major advantage of being low cost and high throughput. Factors influencing GS predictive abilities have been intensively studied, but little is known about PS. We tested and compared the abilities of PS and GS to predict grain yield and heading date from several datasets of bread wheat lines corresponding to the first or second years of trial evaluation from two breeding companies and one research institute in France. We evaluated several factors affecting PS predictive abilities including the possibility of combining spectra collected in different environments. A simple H-BLUP model predicted both traits with prediction ability from 0.26 to 0.62 and with an efficient computation time. Our results showed that the environments in which lines are grown had a crucial impact on predictive ability based on the spectra acquired and was specific to the trait considered. Models combining NIR spectra from different environments were the best PS models and were at least as accurate as GS in most of the datasets. Furthermore, a GH-BLUP model combining genotyping and NIR spectra was the best model of all (prediction ability from 0.31 to 0.73). We demonstrated also that as for GS, the size and the composition of the training set have a crucial impact on predictive ability. PS could therefore replace or complement GS for efficient wheat breeding programs. [ABSTRACT FROM AUTHOR]
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- 2022
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31. Genotyping-by-sequencing and genomic selection applications in hexaploid triticale.
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Ayalew, Habtamu, Anderson, Joshua D., Krom, Nick, Yuhong Tang, Butler, Twain J., Rawat, Nidhi, Tiwari, Vijay, and Xue-Feng Ma
- Subjects
- *
SINGLE nucleotide polymorphisms , *CROP yields , *GENETIC variation , *LINKAGE disequilibrium , *HOMOLOGOUS chromosomes - Abstract
Triticale, a hybrid species between wheat and rye, is one of the newest additions to the plant kingdom with a very short history of improvement. It has very limited genomic resources because of its large and complex genome. Objectives of this study were to generate dense marker data, understand genetic diversity, population structure, linkage disequilibrium (LD), and estimate accuracies of commonly used genomic selection (GS) models on forage yield of triticale. Genotyping-by-sequencing (GBS), using PstI and MspI restriction enzymes for reducing genome complexity, was performed on a triticale diversity panel (n¼289). After filtering for biallelic loci with more than 70% genome coverage, and minor allele frequency (MAF) >0.05, de novo variant calling identified 16,378 single nucleotide polymorphism (SNP) markers. Sequences of these variants were mapped to wheat and rye reference genomes to infer their homologous groups and chromosome positions. About 45% (7430), and 58% (9500) of the de novo identified SNPs were mapped to the wheat and rye reference genomes, respectively. Interestingly, 28.9% (2151) of the 7430 SNPs were mapped to the D genome of hexaploid wheat, indicating substantial substitution of the R genome with D genome in cultivated triticale. About 27% of marker pairs were in significant LD with an average r2>0.18 (P<0.05). Genome-wide LD declined rapidly to r2 < 0.1 beyond 10 kb physical distance. The three sub-genomes (A, B, and R) showed comparable LD decay patterns. Genetic diversity and population structure analyses identified five distinct clusters. Genotype grouping did not follow prior winter vs spring-type classification. However, one of the clusters was largely dominated by winter triticale. GS accuracies were estimated for forage yield using three commonly used models with different training population sizes and marker densities. GS accuracy increased with increasing training population size while gain in accuracy tended to plateau with marker densities of 2000 SNPs or more. Average GS accuracy was about 0.52, indicating the potential of using GS in triticale forage yield improvement. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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32. Genomic Selection for Wheat Blast in a Diversity Panel, Breeding Panel and Full-Sibs Panel.
- Author
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Juliana, Philomin, He, Xinyao, Marza, Felix, Islam, Rabiul, Anwar, Babul, Poland, Jesse, Shrestha, Sandesh, Singh, Gyanendra P., Chawade, Aakash, Joshi, Arun K., Singh, Ravi P., and Singh, Pawan K.
- Subjects
WHEAT breeding ,FIXED effects model ,WHEAT ,BLAST effect ,PREDICTION models - Abstract
Wheat blast is an emerging threat to wheat production, due to its recent migration to South Asia and Sub-Saharan Africa. Because genomic selection (GS) has emerged as a promising breeding strategy, the key objective of this study was to evaluate it for wheat blast phenotyped at precision phenotyping platforms in Quirusillas (Bolivia), Okinawa (Bolivia) and Jashore (Bangladesh) using three panels: (i) a diversity panel comprising 172 diverse spring wheat genotypes, (ii) a breeding panel comprising 248 elite breeding lines, and (iii) a full-sibs panel comprising 298 full-sibs. We evaluated two genomic prediction models (the genomic best linear unbiased prediction or GBLUP model and the Bayes B model) and compared the genomic prediction accuracies with accuracies from a fixed effects model (with selected blast-associated markers as fixed effects), a GBLUP + fixed effects model and a pedigree relationships-based model (ABLUP). On average, across all the panels and environments analyzed, the GBLUP + fixed effects model (0.63 ± 0.13) and the fixed effects model (0.62 ± 0.13) gave the highest prediction accuracies, followed by the Bayes B (0.59 ± 0.11), GBLUP (0.55 ± 0.1), and ABLUP (0.48 ± 0.06) models. The high prediction accuracies from the fixed effects model resulted from the markers tagging the 2NS translocation that had a large effect on blast in all the panels. This implies that in environments where the 2NS translocation-based blast resistance is effective, genotyping one to few markers tagging the translocation is sufficient to predict the blast response and genome-wide markers may not be needed. We also observed that marker-assisted selection (MAS) based on a few blast-associated markers outperformed GS as it selected the highest mean percentage (88.5%) of lines also selected by phenotypic selection and discarded the highest mean percentage of lines (91.8%) also discarded by phenotypic selection, across all panels. In conclusion, while this study demonstrates that MAS might be a powerful strategy to select for the 2NS translocation-based blast resistance, we emphasize that further efforts to use genomic tools to identify non-2NS translocation-based blast resistance are critical. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
33. Genomic Selection for Wheat Blast in a Diversity Panel, Breeding Panel and Full-Sibs Panel
- Author
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Philomin Juliana, Xinyao He, Felix Marza, Rabiul Islam, Babul Anwar, Jesse Poland, Sandesh Shrestha, Gyanendra P. Singh, Aakash Chawade, Arun K. Joshi, Ravi P. Singh, and Pawan K. Singh
- Subjects
wheat ,blast disease ,genomic selection (GS) ,marker-assisted selection ,pedigree selection ,genotyping-by sequencing ,Plant culture ,SB1-1110 - Abstract
Wheat blast is an emerging threat to wheat production, due to its recent migration to South Asia and Sub-Saharan Africa. Because genomic selection (GS) has emerged as a promising breeding strategy, the key objective of this study was to evaluate it for wheat blast phenotyped at precision phenotyping platforms in Quirusillas (Bolivia), Okinawa (Bolivia) and Jashore (Bangladesh) using three panels: (i) a diversity panel comprising 172 diverse spring wheat genotypes, (ii) a breeding panel comprising 248 elite breeding lines, and (iii) a full-sibs panel comprising 298 full-sibs. We evaluated two genomic prediction models (the genomic best linear unbiased prediction or GBLUP model and the Bayes B model) and compared the genomic prediction accuracies with accuracies from a fixed effects model (with selected blast-associated markers as fixed effects), a GBLUP + fixed effects model and a pedigree relationships-based model (ABLUP). On average, across all the panels and environments analyzed, the GBLUP + fixed effects model (0.63 ± 0.13) and the fixed effects model (0.62 ± 0.13) gave the highest prediction accuracies, followed by the Bayes B (0.59 ± 0.11), GBLUP (0.55 ± 0.1), and ABLUP (0.48 ± 0.06) models. The high prediction accuracies from the fixed effects model resulted from the markers tagging the 2NS translocation that had a large effect on blast in all the panels. This implies that in environments where the 2NS translocation-based blast resistance is effective, genotyping one to few markers tagging the translocation is sufficient to predict the blast response and genome-wide markers may not be needed. We also observed that marker-assisted selection (MAS) based on a few blast-associated markers outperformed GS as it selected the highest mean percentage (88.5%) of lines also selected by phenotypic selection and discarded the highest mean percentage of lines (91.8%) also discarded by phenotypic selection, across all panels. In conclusion, while this study demonstrates that MAS might be a powerful strategy to select for the 2NS translocation-based blast resistance, we emphasize that further efforts to use genomic tools to identify non-2NS translocation-based blast resistance are critical.
- Published
- 2022
- Full Text
- View/download PDF
34. Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat (Triticum aestivum L.).
- Author
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Tomar, Vipin, Singh, Daljit, Dhillon, Guriqbal Singh, Chung, Yong Suk, Poland, Jesse, Singh, Ravi Prakash, Joshi, Arun Kumar, Gautam, Yogesh, Tiwari, Budhi Sagar, and Kumar, Uttam
- Subjects
PREDICTION models ,WHEAT breeding ,PLANT breeding ,ENVIRONMENTAL auditing ,GRAIN yields ,WHEAT - Abstract
Genomic selection (GS) has the potential to improve the selection gain for complex traits in crop breeding programs from resource-poor countries. The GS model performance in multi-environment (ME) trials was assessed for 141 advanced breeding lines under four field environments via cross-predictions. We compared prediction accuracy (PA) of two GS models with or without accounting for the environmental variation on four quantitative traits of significant importance, i.e., grain yield (GRYLD), thousand-grain weight, days to heading, and days to maturity, under North and Central Indian conditions. For each trait, we generated PA using the following two different ME cross-validation (CV) schemes representing actual breeding scenarios: (1) predicting untested lines in tested environments through the ME model (ME_CV1) and (2) predicting tested lines in untested environments through the ME model (ME_CV2). The ME predictions were compared with the baseline single-environment (SE) GS model (SE_CV1) representing a breeding scenario, where relationships and interactions are not leveraged across environments. Our results suggested that the ME models provide a clear advantage over SE models in terms of robust trait predictions. Both ME models provided 2–3 times higher prediction accuracies for all four traits across the four tested environments, highlighting the importance of accounting environmental variance in GS models. While the improvement in PA from SE to ME models was significant, the CV1 and CV2 schemes did not show any clear differences within ME, indicating the ME model was able to predict the untested environments and lines equally well. Overall, our results provide an important insight into the impact of environmental variation on GS in smaller breeding programs where these programs can potentially increase the rate of genetic gain by leveraging the ME wheat breeding trials. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
35. Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat (Triticum aestivum L.)
- Author
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Vipin Tomar, Daljit Singh, Guriqbal Singh Dhillon, Yong Suk Chung, Jesse Poland, Ravi Prakash Singh, Arun Kumar Joshi, Yogesh Gautam, Budhi Sagar Tiwari, and Uttam Kumar
- Subjects
single-environment ,multi-environments ,genotyping by sequencing ,genomic selection (GS) ,genomics predictions ,best linear unbiased predictions ,Plant culture ,SB1-1110 - Abstract
Genomic selection (GS) has the potential to improve the selection gain for complex traits in crop breeding programs from resource-poor countries. The GS model performance in multi-environment (ME) trials was assessed for 141 advanced breeding lines under four field environments via cross-predictions. We compared prediction accuracy (PA) of two GS models with or without accounting for the environmental variation on four quantitative traits of significant importance, i.e., grain yield (GRYLD), thousand-grain weight, days to heading, and days to maturity, under North and Central Indian conditions. For each trait, we generated PA using the following two different ME cross-validation (CV) schemes representing actual breeding scenarios: (1) predicting untested lines in tested environments through the ME model (ME_CV1) and (2) predicting tested lines in untested environments through the ME model (ME_CV2). The ME predictions were compared with the baseline single-environment (SE) GS model (SE_CV1) representing a breeding scenario, where relationships and interactions are not leveraged across environments. Our results suggested that the ME models provide a clear advantage over SE models in terms of robust trait predictions. Both ME models provided 2–3 times higher prediction accuracies for all four traits across the four tested environments, highlighting the importance of accounting environmental variance in GS models. While the improvement in PA from SE to ME models was significant, the CV1 and CV2 schemes did not show any clear differences within ME, indicating the ME model was able to predict the untested environments and lines equally well. Overall, our results provide an important insight into the impact of environmental variation on GS in smaller breeding programs where these programs can potentially increase the rate of genetic gain by leveraging the ME wheat breeding trials.
- Published
- 2021
- Full Text
- View/download PDF
36. Genome-wide association study and genomic selection of flax powdery mildew in Xinjiang Province.
- Author
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Zhu L, Li G, Guo D, Li X, Xue M, Jiang H, Yan Q, Xie F, Ning X, and Xie L
- Abstract
Flax powdery mildew (PM), caused by Oidium lini , is a globally distributed fungal disease of flax, and seriously impairs its yield and quality. To data, only three resistance genes and a few putative quantitative trait loci (QTL) have been reported for flax PM resistance. To dissect the resistance mechanism against PM and identify resistant genetic regions, based on four years of phenotypic datasets (2017, 2019 to 2021), a genome-wide association study (GWAS) was performed on 200 flax core accessions using 674,074 SNPs and 7 models. A total of 434 unique quantitative trait nucleotides (QTNs) associated with 331 QTL were detected. Sixty-four loci shared in at least two datasets were found to be significant in haplotype analyses, and 20 of these sites were shared by multiple models. Simultaneously, a large-effect locus ( qDI 11.2 ) was detected repeatedly, which was present in the mapping study of flax pasmo resistance loci. Oil flax had more QTL with positive-effect or favorable alleles (PQTL) and showed higher PM resistance than fiber flax, indicating that effects of these QTL were mainly additive. Furthermore, an excellent resistant variety C120 was identified and can be used to promote planting. Based on 331 QTLs identified through GWAS and the statistical model GBLUP, a genomic selection (GS) model related to flax PM resistance was constructed, and the prediction accuracy rate was 0.96. Our results provide valuable insights into the genetic basis of resistance and contribute to the advancement of breeding programs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhu, Li, Guo, Li, Xue, Jiang, Yan, Xie, Ning and Xie.)
- Published
- 2024
- Full Text
- View/download PDF
37. Editorial: Genomic Selection: Lessons Learned and Perspectives.
- Author
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Martini, Johannes W. R., Hearne, Sarah J., Gardunia, Brian, Wimmer, Valentin, and Toledo, Fernando H.
- Subjects
GENOTYPE-environment interaction ,SEXUAL cycle - Abstract
Another opinion contribution was provided by Gholami et al. who compared the adoption of GS across different breeding institutions, in more detail dairy cattle breeding and public and private plant breeding programs. Keywords: genomic selection (GS); plant breeding; selection gain; breeding schemes; genotype-by-environment interaction EN genomic selection (GS) plant breeding selection gain breeding schemes genotype-by-environment interaction 1 3 3 05/31/22 20220527 NES 220527 Genomic selection (GS) has been one of the most prominent Research Topics in breeding science in the last two decades after the milestone paper by Meuwissen et al. ([1]). Plant breeding, selection gain, genotype-by-environment interaction, genomic selection (GS), breeding schemes. [Extracted from the article]
- Published
- 2022
- Full Text
- View/download PDF
38. Development of a Genomic Prediction Pipeline for Maintaining Comparable Sample Sizes in Training and Testing Sets across Prediction Schemes Accounting for the Genotype-by-Environment Interaction
- Author
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Reyna Persa, Martin Grondona, and Diego Jarquin
- Subjects
genotype-by-environment interaction (G×E) ,genomic prediction (GP) ,genomic prediction pipeline ,genomic selection (GS) ,similar sample sizes for cross-validation schemes ,SoyNAM ,Agriculture (General) ,S1-972 - Abstract
The global growing population is experiencing challenges to satisfy the food chain supply in a world that faces rapid changes in environmental conditions complicating the development of stable cultivars. Emergent methodologies aided by molecular marker information such as marker assisted selection (MAS) and genomic selection (GS) have been widely adopted to assist the development of improved genotypes. In general, the implementation of GS is not straightforward, and it usually requires cross-validation studies to find the optimum set of factors (training set sizes, number of markers, quality control, etc.) to use in real breeding applications. In most cases, these different scenarios (combination of several factors) vary just in the levels of a single factor keeping fixed the levels of the other factors allowing the use of previously developed routines (code reuse). In this study, we present a set of structured modules that are easily to assemble for constructing complex genomic prediction pipelines from scratch. Also, we proposed a novel method for selecting training-testing sets of sizes across different cross-validation schemes (CV2, predicting tested genotypes in observed environments; CV1, predicting untested genotypes in observed environments; CV0, predicting tested genotypes in novel environments; and CV00, predicting untested genotypes in novel environments). To show how our implementation works, we considered two real data sets. These correspond to selected samples of the USDA soybean collection (D1: 324 genotypes observed in 6 environments scored for 9 traits) and of the Soybean Nested Association Mapping (SoyNAM) experiment (D2: 324 genotypes observed in 6 environments scored for 6 traits). In addition, three prediction models which consider the effect of environments and lines (M1: E + L), environments, lines and main effect of markers (M2: E + L + G), and also the inclusion of the interaction between makers and environments (M3: E + L + G + G×E) were considered. The results confirm that under CV2 and CV1 schemes, moderate improvements in predictive ability can be obtained with the inclusion of the interaction component, while for CV0 mixed results were observed, and for CV00 no improvements were shown. However, for this last scenario, the inclusion of weather and soil data potentially could enhance the results of the interaction model.
- Published
- 2021
- Full Text
- View/download PDF
39. Editorial: Accelerating genetic gain for key traits using genome-wide association studies and genomic selection: promising breeding tools for sustainable agriculture.
- Author
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Mishra DC, Budhlakoti N, Juliana P, and Kumar S
- Abstract
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
- Published
- 2023
- Full Text
- View/download PDF
40. Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection
- Author
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Pauline Robert, Jérôme Auzanneau, Ellen Goudemand, François-Xavier Oury, Bernard Rolland, Emmanuel Heumez, Sophie Bouchet, Jacques Le Gouis, Renaud Rincent, Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon), AgroParisTech-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), Agri-obtention (AO), florimont desprez, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Rennes Angers, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Unité Expérimentale Grandes Cultures Innovation Environnement - Picardie (GCIE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and ANRT 2019/0060
- Subjects
[SDV.GEN]Life Sciences [q-bio]/Genetics ,Models, Genetic ,[SDV]Life Sciences [q-bio] ,Genomic-like omics-based (GLOB) prediction ,Genomics ,General Medicine ,Bread wheat ,Phenomic selection (PS) ,Near-infrared spectroscopy (NIRS) ,Plant Breeding ,Phenotype ,Genetics ,Genomic selection (GS) ,[SDV.BV]Life Sciences [q-bio]/Vegetal Biology ,Phenomics ,Selection, Genetic ,Agronomy and Crop Science ,Genome, Plant ,Triticum ,Biotechnology - Abstract
International audience; Phenomic selection is a promising alternative or complement to genomic selection in wheat breeding. Models combining spectra from different environments maximise the predictive ability of grain yield and heading date of wheat breeding lines. Phenomic selection (PS) is a recent breeding approach similar to genomic selection (GS) except that genotyping is replaced by near-infrared (NIR) spectroscopy. PS can potentially account for non-additive effects and has the major advantage of being low cost and high throughput. Factors influencing GS predictive abilities have been intensively studied, but little is known about PS. We tested and compared the abilities of PS and GS to predict grain yield and heading date from several datasets of bread wheat lines corresponding to the first or second years of trial evaluation from two breeding companies and one research institute in France. We evaluated several factors affecting PS predictive abilities including the possibility of combining spectra collected in different environments. A simple H-BLUP model predicted both traits with prediction ability from 0.26 to 0.62 and with an efficient computation time. Our results showed that the environments in which lines are grown had a crucial impact on predictive ability based on the spectra acquired and was specific to the trait considered. Models combining NIR spectra from different environments were the best PS models and were at least as accurate as GS in most of the datasets. Furthermore, a GH-BLUP model combining genotyping and NIR spectra was the best model of all (prediction ability from 0.31 to 0.73). We demonstrated also that as for GS, the size and the composition of the training set have a crucial impact on predictive ability. PS could therefore replace or complement GS for efficient wheat breeding programs.
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- 2022
41. Alternative scoring methods of fusarium head blight resistance for genomic assisted breeding
- Abstract
Fusarium head blight (FHB) is a fungal disease of wheat (Triticum aestivum.L) that causes yield losses and produces mycotoxins which could easily exceed the limits of the EU regulations. Resistance to FHB has a complex genetic architecture and accurate evaluation in breeding programs is key to selecting resistant varieties. The Area Under the Disease Progress Curve (AUDPC) is one of the commonly metric used as a standard methodology to score FHB. Although efficient, AUDPC requires significant costs in phenotyping to cover the entire disease development pattern. Here, we show that there are more efficient alternatives to AUDPC (angle, growing degree days to reach 50% FHB severity, and FHB maximum variance) that reduce the number of field assessments required and allow for fair comparisons between unbalanced evaluations across trials. Furthermore, we found that the evaluation method that captures the maximum variance in FHB severity across plots is the most optimal approach for scoring FHB. In addition, results obtained on experimental data were validated on a simulated experiment where the disease progress curve was modeled as a sigmoid curve with known parameters and assessment protocols were fully controlled. Results show that alternative metrics tested in this study captured key components of quantitative plant resistance. Moreover, the new metrics could be a starting point for more accurate methods for measuring FHB in the field. For example, the optimal interval for FHB evaluation could be predicted using prior knowledge from historical weather data and FHB scores from previous trials. Finally, the evaluation methods presented in this study can reduce the FHB phenotyping burden in plant breeding with minimal losses on signal detection, resulting in a response variable available to use in data-driven analysis such as genome-wide association studies or genomic selection.
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- 2022
42. Genomic selection to improve husk tightness based on genomic molecular markers in maize.
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Liu Y, Ao M, Lu M, Zheng S, Zhu F, Ruan Y, Guan Y, Zhang A, and Cui Z
- Abstract
Introduction: The husk tightness (HTI) in maize plays a crucial role in regulating the water content of ears during the maturity stage, thereby influencing the quality of mechanical grain harvesting in China. Genomic selection (GS), which employs molecular markers, offers a promising approach for identifying and selecting inbred lines with the desired HTI trait in maize breeding. However, the effectiveness of GS is contingent upon various factors, including the genetic architecture of breeding populations, sequencing platforms, and statistical models., Methods: An association panel of maize inbred lines was grown across three sites over two years, divided into four subgroups. GS analysis for HTI prediction was performed using marker data from three sequencing platforms and six marker densities with six statistical methods., Results: The findings indicate that a loosely attached husk can aid in the dissipation of water from kernels in temperate maize germplasms across most environments but not nessarily for tropical-origin maize. Considering the balance between GS prediction accuracy and breeding cost, the optimal prediction strategy is the rrBLUP model, the 50K sequencing platform, a 30% proportion of the test population, and a marker density of r2=0.1. Additionally, selecting a specific SS subgroup for sampling the testing set significantly enhances the predictive capacity for husk tightness., Discussion: The determination of the optimal GS prediction strategy for HTI provides an economically feasible reference for the practice of molecular breeding. It also serves as a reference method for GS breeding of other agronomic traits., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Liu, Ao, Lu, Zheng, Zhu, Ruan, Guan, Zhang and Cui.)
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- 2023
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43. Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials
- Author
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Pauline Robert, Ellen Goudemand, Jérôme Auzanneau, François-Xavier Oury, Bernard Rolland, Emmanuel Heumez, Sophie Bouchet, Antoine Caillebotte, Tristan Mary-Huard, Jacques Le Gouis, Renaud Rincent, Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon), AgroParisTech-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), Agri-obtention (AO), SAS Florimond Desprez Veuve and Fils, Partenaires INRAE, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Rennes Angers, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Domaine expérimental de Brunehaut (LILL MONS UE), Institut National de la Recherche Agronomique (INRA), MIA, and ANRT, Grant Number 2019/0060)
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Genotype ,Models, Genetic ,[SDV]Life Sciences [q-bio] ,General Medicine ,Phenomic selection (PS) ,Plant breeding ,Multi-Environment Trial (MET) ,Triticum aestivum.Bread wheat ,[SDV.GEN.GPL]Life Sciences [q-bio]/Genetics/Plants genetics ,[SDV.BV.AP]Life Sciences [q-bio]/Vegetal Biology/Plant breeding ,Phenotype ,Near infrared spectroscopy (NIRS) ,Genetics ,Genomic selection (GS) ,Gene-Environment Interaction ,Genotype by environment interaction (GxE) ,Phenomics ,Selection, Genetic ,Edible Grain ,Agronomy and Crop Science ,Genome, Plant ,Triticum ,Biotechnology - Abstract
International audience; Key message Phenomic prediction of wheat grain yield and heading date in different multi-environmental trial scenarios is accurate. Modelling the genotype-by-environment interaction effect using phenomic data is a potentially low-cost complement to genomic prediction. The performance of wheat cultivars in multi-environmental trials (MET) is difficult to predict because of the genotype-by-environment interactions (G x E). Phenomic selection is supposed to be efficient for modelling the G x E effect because it accounts for non-additive effects. Here, phenomic data are near-infrared (NIR) spectra obtained from plant material. While phenomic selection has recently been shown to accurately predict wheat grain yield in single environments, its accuracy needs to be investigated for MET. We used four datasets from two winter wheat breeding programs to test and compare the predictive abilities of phenomic and genomic models for grain yield and heading date in different MET scenarios. We also compared different methods to model the G x E using different covariance matrices based on spectra. On average, phenomic and genomic prediction abilities are similar in all different MET scenarios. Better predictive abilities were obtained when G x E effects were modelled with NIR spectra than without them, and it was better to use all the spectra of all genotypes in all environments for modelling the G x E. To facilitate the implementation of phenomic prediction, we tested MET designs where the NIR spectra were measured only on the genotype-environment combinations phenotyped for the target trait. Missing spectra were predicted with a weighted multivariate ridge regression. Intermediate predictive abilities for grain yield were obtained in a sparse testing scenario and for new genotypes, which shows that phenomic selection is an efficient and practicable prediction method for dealing with G x E.
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- 2022
44. Phenomic Selection: A new and efficient alternative to genomic selection
- Author
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Pauline Robert, Charlotte Brault, Renaud Rincent, Vincent Segura, Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon), AgroParisTech-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Montpellier (UM), Géno-vigne® (UMT Géno-vigne®), Institut Français de la Vigne et du Vin (IFV)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), fond CASDAR,C-2020-5, ANRT, grant number 2018/0577, ANRT, grant number 2019/0060, ANR-19-ECOM-0006,SelGenVit,Sélection génomique au service de l'amélioration de la vigne pour la diversification et le déploiement de variétés résistantes à forts potentiel œnologique(2019), and ANR-10-BTBR-0003,BREEDWHEAT,Développer de nouvelles variétés de blé pour une agriculture durable(2010)
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[SDV.GEN.GPL]Life Sciences [q-bio]/Genetics/Plants genetics ,Hyperspectral imaging ,[SDV]Life Sciences [q-bio] ,Genomic selection (GS) ,[SDV.BV]Life Sciences [q-bio]/Vegetal Biology ,Plant breeding ,Phenomic selection (PS) ,Genomic-like omics-based (GLOB) selection ,Near-infrared spectroscopy (NIRS) - Abstract
Recently, it has been proposed to switch molecular markers to near-infrared (NIR) spectra for inferring relationships between individuals and further performing phenomic selection (PS), analogous to genomic selection (GS). The PS concept is similar to genomic-like omics-based (GLOB) selection, in which molecular markers are replaced by endophenotypes, such as metabolites or transcript levels, except that the phenomic information obtained for instance by near-infrared spectroscopy (NIRS) has usually a much lower cost than other omics. Though NIRS has been routinely used in breeding for several decades, especially to deal with end-product quality traits, its use to predict other traits of interest and further make selections is new. Since the seminal paper on PS, several publications have advocated the use of spectral acquisition (including NIRS and hyperspectral imaging) in plant breeding towards PS, potentially providing a scope of what is possible. In the present chapter, we first come back to the concept of PS as originally proposed and provide a classification of selected papers related to the use of phenomics in breeding. We further provide a review of the selected literature concerning the type of technology used, the preprocessing of the spectra, and the statistical modeling to make predictions. We discuss the factors that likely affect the efficiency of PS and compare it to GS in terms of predictive ability. Finally, we propose several prospects for future work and application of PS in the context of plant breeding.
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- 2022
45. Applied phenomics and genomics for improving barley yellow dwarf resistance in winter wheat
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Paula Silva, Byron Evers, Alexandria Kieffaber, Xu Wang, Richard Brown, Liangliang Gao, Allan Fritz, Jared Crain, and Jesse Poland
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Insecticides ,Barley yellow dwarf (BYD) ,Resistance ,Quantitative Trait Loci ,Triticum aestivum ,food and beverages ,Hordeum ,Genomics ,High-throughput Phenotyping (HTP) ,Virus ,Plant Breeding ,Phenotype ,Genomic Selection (GS) ,Genetics ,Seasons ,Phenomics ,Edible Grain ,Molecular Biology ,Tolerance ,Genetics (clinical) ,Triticum ,Plant Diseases - Abstract
Barley yellow dwarf is one of the major viral diseases of cereals. Phenotyping barley yellow dwarf in wheat is extremely challenging due to similarities to other biotic and abiotic stresses. Breeding for resistance is additionally challenging as the wheat primary germplasm pool lacks genetic resistance, with most of the few resistance genes named to date originating from a wild relative species. The objectives of this study were to (1) evaluate the use of high-throughput phenotyping to improve barley yellow dwarf assessment; (2) identify genomic regions associated with barley yellow dwarf resistance; and (3) evaluate the ability of genomic selection models to predict barley yellow dwarf resistance. Up to 107 wheat lines were phenotyped during each of 5 field seasons under both insecticide treated and untreated plots. Across all seasons, barley yellow dwarf severity was lower within the insecticide treatment along with increased plant height and grain yield compared with untreated entries. Only 9.2% of the lines were positive for the presence of the translocated segment carrying the resistance gene Bdv2. Despite the low frequency, this region was identified through association mapping. Furthermore, we mapped a potentially novel genomic region for barley yellow dwarf resistance on chromosome 5AS. Given the variable heritability of the trait (0.211–0.806), we obtained a predictive ability for barley yellow dwarf severity ranging between 0.06 and 0.26. Including the presence or absence of Bdv2 as a covariate in the genomic selection models had a large effect for predicting barley yellow dwarf but almost no effect for other observed traits. This study was the first attempt to characterize barley yellow dwarf using field-high-throughput phenotyping and apply genomic selection to predict disease severity. These methods have the potential to improve barley yellow dwarf characterization, additionally identifying new sources of resistance will be crucial for delivering barley yellow dwarf resistant germplasm.
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- 2022
46. Alternative scoring methods of fusarium head blight resistance for genomic assisted breeding
- Author
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Garcia-Abadillo, J., Morales, L., Buerstmayr, H., Michel, S., Lillemo, M., Holzapfel, J., Hartl, L., Akdemir, D., Carvalho, H. F., Isidro-Sánchez, J., European Commission, Ministerio de Educación y Formación Profesional (España), Agencia Estatal de Investigación (España), Universidad Politécnica de Madrid, Research Council of Norway, Garcia-Abadillo, J., Buerstmayr, H., Maltoni, Michele, Lillemo, M., Hartl, L., and Isidro-Sánchez, Julio
- Subjects
Simulation and empirical evidence ,Quantitative resistance ,Wheat ,Genomic selection (GS) ,Plant Science ,Fusarium head blight (FHB) ,Plant breeding - Abstract
14 Pág., Fusarium head blight (FHB) is a fungal disease of wheat (Triticum aestivum.L) that causes yield losses and produces mycotoxins which could easily exceed the limits of the EU regulations. Resistance to FHB has a complex genetic architecture and accurate evaluation in breeding programs is key to selecting resistant varieties. The Area Under the Disease Progress Curve (AUDPC) is one of the commonly metric used as a standard methodology to score FHB. Although efficient, AUDPC requires significant costs in phenotyping to cover the entire disease development pattern. Here, we show that there are more efficient alternatives to AUDPC (angle, growing degree days to reach 50% FHB severity, and FHB maximum variance) that reduce the number of field assessments required and allow for fair comparisons between unbalanced evaluations across trials. Furthermore, we found that the evaluation method that captures the maximum variance in FHB severity across plots is the most optimal approach for scoring FHB. In addition, results obtained on experimental data were validated on a simulated experiment where the disease progress curve was modeled as a sigmoid curve with known parameters and assessment protocols were fully controlled. Results show that alternative metrics tested in this study captured key components of quantitative plant resistance. Moreover, the new metrics could be a starting point for more accurate methods for measuring FHB in the field. For example, the optimal interval for FHB evaluation could be predicted using prior knowledge from historical weather data and FHB scores from previous trials. Finally, the evaluation methods presented in this study can reduce the FHB phenotyping burden in plant breeding with minimal losses on signal detection, resulting in a response variable available to use in data-driven analysis such as genome-wide association studies or genomic selection., The WheatSustain project was carried out under the ERA-NET Cofund SusCrop (Horizon 2020 Grant No 771134), being part of the Joint Programming Initiative on Agriculture, Food Security and Climate Change (FACCE-JPI). I-SJ was supported by the Beatriz GalindoProgram (BEAGAL18/00115) from the Ministerio de Educación y Formación Profesional of Spain and the Severo Ochoa Program for Centres of Excellence in R&D from the Agencia Estatal de Investigación of Spain, grant SEV-2016-0672 (2017-2021) to the CBGP. G-AJ is working under a UPM predoctoral grant as part of the programme “Programa Propio I+D+i” financed by the Universidad Politécnica de Madrid. ML, MS, and BH were supported by the Austrian Federal Ministry of Agriculture, Regions and Tourism (grantnumber DaFNE-101402). LM was supported by the Research Council of Norway (NFR grant 299615), and and HL by Deutsches Bundesministerium für Bildung und Forschung (031B0810).
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- 2022
47. Identification of factors influencing predictive ability of phenomic selection and comparison to genomic selection in wheat breeding programs
- Author
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ROBERT, P, Oury, Francois-Xavier, Auzanneau, Jérôme, Rolland, Bernard, Heumez, Emmanuel, Bouchet, Sophie, Le Gouis, Jacques, Rincent, Renaud, Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon), AgroParisTech-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Génétique Diversité et Ecophysiologie des Céréales (GDEC), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), Agri-obtention (AO), Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Unité Expérimentale Grandes Cultures Innovation Environnement - Picardie (GCIE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), EUCARPIA, AKCongress, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, and Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
- Subjects
phenomic selection (PS) ,genomic selection (GS) ,[SDV]Life Sciences [q-bio] ,bread wheat ,plant breeding ,near infrared spectroscopy (NIRS) ,genomic-like omics-based (GLOB) prediction - Abstract
International audience; In plant breeding, the selection of the best individuals is mainly based on phenotyping records. Because phenotyping is costly and time consuming, predictive tools such as Genomic selection (GS) have been developed in order to select among unphenotyped candidates. GS allows predicting the target traits for the selection candidates using the phenotypes of a training set and genotypic information collected on the training set and the selection candidates. Despite a good potential of the method to assist breeders in their selection choices, the cost of the genotyping still remains expensive, as GS requires to genotype each year the new selection candidates. In 2018, Rincent et al. developed a new, low cost, and high throughput method to predict the target trait of unobserved selection candidates. This method called phenomic selection (PS) is similar to GS, but genotyping is replaced by near infrared spectroscopy (NIRS). NIRS has the main advantage of being affordable, and already routinely applied on the selection candidates for many species such as wheat. GS has been well studied for twenty years, and many factors influencing its predictive ability are well understood. In PS, little is known about the factors influencing the predictive abilities, and about its performance relative to GS. We conducted the analyses on several datasets, corresponding to breeding lines drawn from the first or second years of trial evaluation from two breeding companies and one research institute in France. We evaluated several factors affecting PS predictive abilities including the possibility of combining spectra collected in different environments or at different steps of the breeding program. Contrary to genotypic data, near infrared spectra are indeed influenced by both the genotype and the environment. Thus, a selection candidate can be characterised by a multitude of spectra measured in different environments. The statistical model used was a simple H-BLUP model, reaching prediction ability from 0.26 to 0.62.Our results showed that the environment in which the NIR spectra was collected had an impor-tant impact on predictive ability and this impact was specific to the trait considered. Among all the models tested, combining NIR spectra from different environments were the best PS models and were at least as accurate as GS in most of the datasets. We finally tested a model which gathered NIRS and molecular marker effects. This model, GH-BLUP, was the best model of all, regardless of the trait or dataset, with prediction abilities reaching 0.31 to 0.73. In this study we showed that PS could be a great support tool for breeders to improve wheat breeding programs and could efficiently replace or complement GS..
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- 2021
48. Development of a Genomic Prediction Pipeline for Maintaining Comparable Sample Sizes in Training and Testing Sets across Prediction Schemes Accounting for the Genotype-by-Environment Interaction
- Author
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Martin Grondona, Reyna Persa, and Diego Jarquin
- Subjects
Computer science ,Agriculture (General) ,Population ,Plant Science ,genomic prediction pipeline ,S1-972 ,Set (abstract data type) ,chemistry.chemical_compound ,Molecular marker ,Statistics ,genotype-by-environment interaction (G×E) ,Nested association mapping ,Gene–environment interaction ,education ,education.field_of_study ,genomic selection (GS) ,USDA soybean collection ,SoyNAM ,genomic prediction (GP) ,chemistry ,Sample size determination ,Main effect ,Agronomy and Crop Science ,similar sample sizes for cross-validation schemes ,Predictive modelling ,Food Science - Abstract
The global growing population is experiencing challenges to satisfy the food chain supply in a world that faces rapid changes in environmental conditions complicating the development of stable cultivars. Emergent methodologies aided by molecular marker information such as marker assisted selection (MAS) and genomic selection (GS) have been widely adopted to assist the development of improved genotypes. In general, the implementation of GS is not straightforward, and it usually requires cross-validation studies to find the optimum set of factors (training set sizes, number of markers, quality control, etc.) to use in real breeding applications. In most cases, these different scenarios (combination of several factors) vary just in the levels of a single factor keeping fixed the levels of the other factors allowing the use of previously developed routines (code reuse). In this study, we present a set of structured modules that are easily to assemble for constructing complex genomic prediction pipelines from scratch. Also, we proposed a novel method for selecting training-testing sets of sizes across different cross-validation schemes (CV2, predicting tested genotypes in observed environments, CV1, predicting untested genotypes in observed environments, CV0, predicting tested genotypes in novel environments, and CV00, predicting untested genotypes in novel environments). To show how our implementation works, we considered two real data sets. These correspond to selected samples of the USDA soybean collection (D1: 324 genotypes observed in 6 environments scored for 9 traits) and of the Soybean Nested Association Mapping (SoyNAM) experiment (D2: 324 genotypes observed in 6 environments scored for 6 traits). In addition, three prediction models which consider the effect of environments and lines (M1: E + L), environments, lines and main effect of markers (M2: E + L + G), and also the inclusion of the interaction between makers and environments (M3: E + L + G + G×E) were considered. The results confirm that under CV2 and CV1 schemes, moderate improvements in predictive ability can be obtained with the inclusion of the interaction component, while for CV0 mixed results were observed, and for CV00 no improvements were shown. However, for this last scenario, the inclusion of weather and soil data potentially could enhance the results of the interaction model.
- Published
- 2021
- Full Text
- View/download PDF
49. Development of a Genomic Prediction Pipeline for Maintaining Comparable Sample Sizes in Training and Testing Sets across Prediction Schemes Accounting for the Genotype-by-Environment Interaction.
- Author
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Persa, Reyna, Grondona, Martin, and Jarquin, Diego
- Subjects
SAMPLE size (Statistics) ,FORECASTING ,PREDICTION models ,SOIL weathering ,FOOD supply - Abstract
The global growing population is experiencing challenges to satisfy the food chain supply in a world that faces rapid changes in environmental conditions complicating the development of stable cultivars. Emergent methodologies aided by molecular marker information such as marker assisted selection (MAS) and genomic selection (GS) have been widely adopted to assist the development of improved genotypes. In general, the implementation of GS is not straightforward, and it usually requires cross-validation studies to find the optimum set of factors (training set sizes, number of markers, quality control, etc.) to use in real breeding applications. In most cases, these different scenarios (combination of several factors) vary just in the levels of a single factor keeping fixed the levels of the other factors allowing the use of previously developed routines (code reuse). In this study, we present a set of structured modules that are easily to assemble for constructing complex genomic prediction pipelines from scratch. Also, we proposed a novel method for selecting training-testing sets of sizes across different cross-validation schemes (CV2, predicting tested genotypes in observed environments; CV1, predicting untested genotypes in observed environments; CV0, predicting tested genotypes in novel environments; and CV00, predicting untested genotypes in novel environments). To show how our implementation works, we considered two real data sets. These correspond to selected samples of the USDA soybean collection (D1: 324 genotypes observed in 6 environments scored for 9 traits) and of the Soybean Nested Association Mapping (SoyNAM) experiment (D2: 324 genotypes observed in 6 environments scored for 6 traits). In addition, three prediction models which consider the effect of environments and lines (M1: E + L), environments, lines and main effect of markers (M2: E + L + G), and also the inclusion of the interaction between makers and environments (M3: E + L + G + G×E) were considered. The results confirm that under CV2 and CV1 schemes, moderate improvements in predictive ability can be obtained with the inclusion of the interaction component, while for CV0 mixed results were observed, and for CV00 no improvements were shown. However, for this last scenario, the inclusion of weather and soil data potentially could enhance the results of the interaction model. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
50. Phenomic Selection: A New and Efficient Alternative to Genomic Selection.
- Author
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Robert P, Brault C, Rincent R, and Segura V
- Subjects
- Genome, Plant, Genomics methods, Phenotype, Selection, Genetic, Phenomics, Plant Breeding methods
- Abstract
Recently, it has been proposed to switch molecular markers to near-infrared (NIR) spectra for inferring relationships between individuals and further performing phenomic selection (PS), analogous to genomic selection (GS). The PS concept is similar to genomic-like omics-based (GLOB) selection, in which molecular markers are replaced by endophenotypes, such as metabolites or transcript levels, except that the phenomic information obtained for instance by near-infrared spectroscopy (NIRS ) has usually a much lower cost than other omics. Though NIRS has been routinely used in breeding for several decades, especially to deal with end-product quality traits, its use to predict other traits of interest and further make selections is new. Since the seminal paper on PS , several publications have advocated the use of spectral acquisition (including NIRS and hyperspectral imaging) in plant breeding towards PS , potentially providing a scope of what is possible. In the present chapter, we first come back to the concept of PS as originally proposed and provide a classification of selected papers related to the use of phenomics in breeding. We further provide a review of the selected literature concerning the type of technology used, the preprocessing of the spectra, and the statistical modeling to make predictions. We discuss the factors that likely affect the efficiency of PS and compare it to GS in terms of predictive ability. Finally, we propose several prospects for future work and application of PS in the context of plant breeding., (© 2022. The Author(s).)
- Published
- 2022
- Full Text
- View/download PDF
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