دورية أكاديمية
أعرض في EDS

A joint learning approach for genomic prediction in polyploid grasses.

التفاصيل البيبلوغرافية
العنوان: A joint learning approach for genomic prediction in polyploid grasses.
المؤلفون: Aono AH; Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, SP, Brazil.; The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK., Ferreira RCU; Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, SP, Brazil., Moraes ADCL; Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, SP, Brazil., Lara LAC; Genetics Department, Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo (USP), Piracicaba, SP, Brazil., Pimenta RJG; Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, SP, Brazil., Costa EA; Instituto de Ciência e Tecnologia (ICT), Universidade Federal de São Paulo (UNIFESP), São José dos Campos, SP, Brazil., Pinto LR; Advanced Center of Sugarcane Agrobusiness Technological Research, Agronomic Institute of Campinas (IAC), Ribeirão Preto, SP, Brazil., Landell MGA; Advanced Center of Sugarcane Agrobusiness Technological Research, Agronomic Institute of Campinas (IAC), Ribeirão Preto, SP, Brazil., Santos MF; Embrapa Beef Cattle, Brazilian Agricultural Research Corporation, Campo Grande, MS, Brazil., Jank L; Embrapa Beef Cattle, Brazilian Agricultural Research Corporation, Campo Grande, MS, Brazil., Barrios SCL; Embrapa Beef Cattle, Brazilian Agricultural Research Corporation, Campo Grande, MS, Brazil., do Valle CB; Embrapa Beef Cattle, Brazilian Agricultural Research Corporation, Campo Grande, MS, Brazil., Chiari L; Embrapa Beef Cattle, Brazilian Agricultural Research Corporation, Campo Grande, MS, Brazil., Garcia AAF; Genetics Department, Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo (USP), Piracicaba, SP, Brazil., Kuroshu RM; Instituto de Ciência e Tecnologia (ICT), Universidade Federal de São Paulo (UNIFESP), São José dos Campos, SP, Brazil., Lorena AC; Aeronautics Institute of Technology, São José dos Campos, SP, Brazil., Gorjanc G; The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK., de Souza AP; Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, SP, Brazil. anete@unicamp.br.; Department of Plant Biology, Institute of Biology (IB), University of Campinas (UNICAMP), Campinas, SP, Brazil. anete@unicamp.br.
المصدر: Scientific reports [Sci Rep] 2022 Jul 21; Vol. 12 (1), pp. 12499. Date of Electronic Publication: 2022 Jul 21.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
أسماء مطبوعة: Original Publication: London : Nature Publishing Group, copyright 2011-
مواضيع طبية MeSH: Poaceae*/genetics , Saccharum*/genetics, Genomics/methods ; Phenotype ; Plant Breeding ; Polyploidy
مستخلص: Poaceae, among the most abundant plant families, includes many economically important polyploid species, such as forage grasses and sugarcane (Saccharum spp.). These species have elevated genomic complexities and limited genetic resources, hindering the application of marker-assisted selection strategies. Currently, the most promising approach for increasing genetic gains in plant breeding is genomic selection. However, due to the polyploidy nature of these polyploid species, more accurate models for incorporating genomic selection into breeding schemes are needed. This study aims to develop a machine learning method by using a joint learning approach to predict complex traits from genotypic data. Biparental populations of sugarcane and two species of forage grasses (Urochloa decumbens, Megathyrsus maximus) were genotyped, and several quantitative traits were measured. High-quality markers were used to predict several traits in different cross-validation scenarios. By combining classification and regression strategies, we developed a predictive system with promising results. Compared with traditional genomic prediction methods, the proposed strategy achieved accuracy improvements exceeding 50%. Our results suggest that the developed methodology could be implemented in breeding programs, helping reduce breeding cycles and increase genetic gains.
(© 2022. The Author(s).)
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تواريخ الأحداث: Date Created: 20220721 Date Completed: 20220725 Latest Revision: 20221115
رمز التحديث: 20221213
مُعرف محوري في PubMed: PMC9304331
DOI: 10.1038/s41598-022-16417-7
PMID: 35864135
قاعدة البيانات: MEDLINE
الوصف
تدمد:2045-2322
DOI:10.1038/s41598-022-16417-7