Joyce N. Njuguna, Lindsay V. Clark, Alexander E. Lipka, Kossonou G. Anzoua, Larisa Bagmet, Pavel Chebukin, Maria S. Dwiyanti, Elena Dzyubenko, Nicolay Dzyubenko, Bimal Kumar Ghimire, Xiaoli Jin, Douglas A. Johnson, Hironori Nagano, Junhua Peng, Karen Koefoed Petersen, Andrey Sabitov, Eun Soo Seong, Toshihiko Yamada, Ji Hye Yoo, Chang Yeon Yu, Hua Zhao, Stephen P. Long, Erik J. Sacks
{"title":"芒草产量和成分性状的全基因组关联及基因组预测","authors":"Joyce N. Njuguna, Lindsay V. Clark, Alexander E. Lipka, Kossonou G. Anzoua, Larisa Bagmet, Pavel Chebukin, Maria S. Dwiyanti, Elena Dzyubenko, Nicolay Dzyubenko, Bimal Kumar Ghimire, Xiaoli Jin, Douglas A. Johnson, Hironori Nagano, Junhua Peng, Karen Koefoed Petersen, Andrey Sabitov, Eun Soo Seong, Toshihiko Yamada, Ji Hye Yoo, Chang Yeon Yu, Hua Zhao, Stephen P. Long, Erik J. Sacks","doi":"10.1111/gcbb.13097","DOIUrl":null,"url":null,"abstract":"<p>Accelerating biomass improvement is a major goal of <i>Miscanthus</i> breeding. The development and implementation of genomic-enabled breeding tools, like marker-assisted selection (MAS) and genomic selection, has the potential to improve the efficiency of <i>Miscanthus</i> breeding. The present study conducted genome-wide association (GWA) and genomic prediction of biomass yield and 14 yield-components traits in <i>Miscanthus sacchariflorus</i>. We evaluated a diversity panel with 590 accessions of <i>M. sacchariflorus</i> grown across 4 years in one subtropical and three temperate locations and genotyped with 268,109 single-nucleotide polymorphisms (SNPs). The GWA study identified a total of 835 significant SNPs and 674 candidate genes across all traits and locations. Of the significant SNPs identified, 280 were localized in mapped quantitative trait loci intervals and proximal to SNPs identified for similar traits in previously reported <i>Miscanthus</i> studies, providing additional support for the importance of these genomic regions for biomass yield. Our study gave insights into the genetic basis for yield-component traits in <i>M. sacchariflorus</i> that may facilitate marker-assisted breeding for biomass yield. Genomic prediction accuracy for the yield-related traits ranged from 0.15 to 0.52 across all locations and genetic groups. Prediction accuracies within the six genetic groupings of <i>M. sacchariflorus</i> were limited due to low sample sizes. Nevertheless, the Korea/NE China/Russia (<i>N</i> = 237) genetic group had the highest prediction accuracy of all genetic groups (ranging 0.26–0.71), suggesting that with adequate sample sizes, there is strong potential for genomic selection within the genetic groupings of <i>M. sacchariflorus</i>. This study indicated that MAS and genomic prediction will likely be beneficial for conducting population-improvement of <i>M. sacchariflorus</i>.</p>","PeriodicalId":55126,"journal":{"name":"Global Change Biology Bioenergy","volume":"15 11","pages":"1355-1372"},"PeriodicalIF":5.9000,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gcbb.13097","citationCount":"0","resultStr":"{\"title\":\"Genome-wide association and genomic prediction for yield and component traits of Miscanthus sacchariflorus\",\"authors\":\"Joyce N. Njuguna, Lindsay V. Clark, Alexander E. Lipka, Kossonou G. Anzoua, Larisa Bagmet, Pavel Chebukin, Maria S. Dwiyanti, Elena Dzyubenko, Nicolay Dzyubenko, Bimal Kumar Ghimire, Xiaoli Jin, Douglas A. 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The GWA study identified a total of 835 significant SNPs and 674 candidate genes across all traits and locations. Of the significant SNPs identified, 280 were localized in mapped quantitative trait loci intervals and proximal to SNPs identified for similar traits in previously reported <i>Miscanthus</i> studies, providing additional support for the importance of these genomic regions for biomass yield. Our study gave insights into the genetic basis for yield-component traits in <i>M. sacchariflorus</i> that may facilitate marker-assisted breeding for biomass yield. Genomic prediction accuracy for the yield-related traits ranged from 0.15 to 0.52 across all locations and genetic groups. Prediction accuracies within the six genetic groupings of <i>M. sacchariflorus</i> were limited due to low sample sizes. Nevertheless, the Korea/NE China/Russia (<i>N</i> = 237) genetic group had the highest prediction accuracy of all genetic groups (ranging 0.26–0.71), suggesting that with adequate sample sizes, there is strong potential for genomic selection within the genetic groupings of <i>M. sacchariflorus</i>. 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Genome-wide association and genomic prediction for yield and component traits of Miscanthus sacchariflorus
Accelerating biomass improvement is a major goal of Miscanthus breeding. The development and implementation of genomic-enabled breeding tools, like marker-assisted selection (MAS) and genomic selection, has the potential to improve the efficiency of Miscanthus breeding. The present study conducted genome-wide association (GWA) and genomic prediction of biomass yield and 14 yield-components traits in Miscanthus sacchariflorus. We evaluated a diversity panel with 590 accessions of M. sacchariflorus grown across 4 years in one subtropical and three temperate locations and genotyped with 268,109 single-nucleotide polymorphisms (SNPs). The GWA study identified a total of 835 significant SNPs and 674 candidate genes across all traits and locations. Of the significant SNPs identified, 280 were localized in mapped quantitative trait loci intervals and proximal to SNPs identified for similar traits in previously reported Miscanthus studies, providing additional support for the importance of these genomic regions for biomass yield. Our study gave insights into the genetic basis for yield-component traits in M. sacchariflorus that may facilitate marker-assisted breeding for biomass yield. Genomic prediction accuracy for the yield-related traits ranged from 0.15 to 0.52 across all locations and genetic groups. Prediction accuracies within the six genetic groupings of M. sacchariflorus were limited due to low sample sizes. Nevertheless, the Korea/NE China/Russia (N = 237) genetic group had the highest prediction accuracy of all genetic groups (ranging 0.26–0.71), suggesting that with adequate sample sizes, there is strong potential for genomic selection within the genetic groupings of M. sacchariflorus. This study indicated that MAS and genomic prediction will likely be beneficial for conducting population-improvement of M. sacchariflorus.
期刊介绍:
GCB Bioenergy is an international journal publishing original research papers, review articles and commentaries that promote understanding of the interface between biological and environmental sciences and the production of fuels directly from plants, algae and waste. The scope of the journal extends to areas outside of biology to policy forum, socioeconomic analyses, technoeconomic analyses and systems analysis. Papers do not need a global change component for consideration for publication, it is viewed as implicit that most bioenergy will be beneficial in avoiding at least a part of the fossil fuel energy that would otherwise be used.
Key areas covered by the journal:
Bioenergy feedstock and bio-oil production: energy crops and algae their management,, genomics, genetic improvements, planting, harvesting, storage, transportation, integrated logistics, production modeling, composition and its modification, pests, diseases and weeds of feedstocks. Manuscripts concerning alternative energy based on biological mimicry are also encouraged (e.g. artificial photosynthesis).
Biological Residues/Co-products: from agricultural production, forestry and plantations (stover, sugar, bio-plastics, etc.), algae processing industries, and municipal sources (MSW).
Bioenergy and the Environment: ecosystem services, carbon mitigation, land use change, life cycle assessment, energy and greenhouse gas balances, water use, water quality, assessment of sustainability, and biodiversity issues.
Bioenergy Socioeconomics: examining the economic viability or social acceptability of crops, crops systems and their processing, including genetically modified organisms [GMOs], health impacts of bioenergy systems.
Bioenergy Policy: legislative developments affecting biofuels and bioenergy.
Bioenergy Systems Analysis: examining biological developments in a whole systems context.