[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125998-en":3,"doc-seo-125998-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125998,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",6,"Technology","ShinyGS—a graphical toolkit with a serial of genetic and machine learning models for genomic selection - application, benchmarking, and recommendations","Genomic prediction accelerates genetic gain and shortens breeding cycles in animal and crop improvement, yet practical use of statistical and machine learning models often requires advanced R skills and command-line workflows for quality control, input formatting, and dependency installation. ShinyGS provides a stand-alone R Shiny application with a point-and-click interface for genomic selection, integrating 16 prediction methods and visualization. The toolkit benchmarks all models across multiple populations and traits with diverse genetic architectures and offers recommendations for specific breeding scenarios, supporting fast installation via Docker.","TYPE Technology and Code PUBLISHED 24 December 2024 DOI 10.3389/fpls.2024.1480902  \nOPEN ACCESS  \nEDITED BY  \nGeorge V. Popescu,  \nMississippi State University, United States  \nREVIEWED BY  \nJuliana Petrini,  \nClinica do Leite Ltda, Brazil Guoqing Tang,  \nSichuan Agricultural University, China  \n*CORRESPONDENCE  \nTao Zhao  \n [tao.zhao@nwafu.edu.cn](tao.zhao@nwafu.edu.cn)[ ](tao.zhao@nwafu.edu.cn)Yanjun Zan  \n [zanyanjun@caas.cn](zanyanjun@caas.cn)  \n†These authors have contributed equally to this work  \nRECEIVED 14 August 2024  \nACCEPTED 02 December 2024  \nPUBLISHED 24 December 2024  \nCITATION  \nYu L, Dai Y, Zhu M, Guo L, Ji Y, Si H, Cheng L, Zhao T and Zan Y (2024) ShinyGS—a graphical toolkit with a serial of genetic and machine learning models for genomic selection: application, benchmarking, and recommendations.  \nFront. Plant Sci. 15:1480902 .  \ndoi: 10.3389/fpls.2024.1480902  \nCOPYRIGHT  \n© 2024 Yu, Dai, Zhu, Guo, Ji, Si, Cheng, Zhao and Zan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nShinyGS—a graphical toolkit with a serial of genetic and machine learning models for genomic selection: application, benchmarking, and recommendations  \nLe Yu1,2†, Yifei Dai 3†, Mingjia Zhu 4†, Linjie Guo 1, Yan Ji 1, Huan Si 1, Lirui Cheng 1, Tao Zhao 5* and Yanjun Zan 1*  \n1Tobacco Research Institute, Chinese Academy of Agricultural Sciences, Qingdao, China,  \n2 Department of Plant Biology, Swedish University of Agriculture Sciences, Uppsala, Sweden,  \n3 Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States, 4College of Ecology, Lanzhou University, Lanzhou, China, 5College of Horticulture, Northwest Agriculture and Forestry University, Yangling, China  \nGenomic prediction is a powerful approach for improving genetic gain and shortening the breeding cycles in animal and crop breeding programs. A series of statistical and machine learning models has been developed to increase the prediction performance continuously. However, the application of these models requires advanced R programming skills and command-line tools to perform quality control, format input files, and install packages and dependencies, posing challenges for breeders. Here, we present ShinyGS, a stand-alone R Shiny application with a user-friendly interface that allows breeders to perform genomic selection through simple point-and-click actions . This toolkit incorporates 16 methods, including linear models from maximum likelihood and Bayesian framework (BA, BB, BC, BL, and BRR), machine learning models, and a data visualization function. In addition, we benchmarked the performance of all 16 models using multiple populations and traits with varying populations and genetic architecture. Recommendations were given for specific breeding applications. Overall, ShinyGS is a platform-independent software that can be run on all operating systems with a Docker container for quick installation. It is freely available to non-commercial users at Docker Hub ([https://hub.docker.com/r/](https://hub.docker.com/r/)[ ](https://hub.docker.com/r/)[yfd2/ags](yfd2/ags)) .  \nKEYWORDS  \ngenomic prediction, BLUP, machine learning, breeding, graphical toolkit  \nFrontiers in Plant Science 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nPolygenic traits are influenced by multiple genes, leading to continuously distributed phenotypes, such as plant height, grain yield, and resistance to diseases. Accurate predictions of these traits can help crop and animal breeders develop varieties and breeds with signific","cbCaip5Xp4MofoBY","https://ap.wps.com/l/cbCaip5Xp4MofoBY","pdf",2429094,10,1,11,"English","en",105,"# Introduction\n## Genomic prediction background and challenges\n## ShinyGS toolkit overview","[{\"question\":\"What problem does ShinyGS address for genomic selection?\",\"answer\":\"It removes the need for advanced R programming and command-line steps by offering a user-friendly interface for quality control, input preparation, package handling, and running genomic selection models.\"},{\"question\":\"What methods are included in ShinyGS?\",\"answer\":\"ShinyGS incorporates 16 genomic prediction methods spanning linear models (e.g., rrBLUP within different Bayesian/maximum-likelihood frameworks), machine learning models, and a data visualization function.\"},{\"question\":\"How were the models evaluated and what output does the toolkit provide?\",\"answer\":\"The toolkit benchmarks performance across multiple populations and traits under varying population sizes and genetic architectures, and it gives recommendations tailored to specific breeding applications.\"}]","ShinyGS—a graphical toolkit with a serial of genetic and machine learning models for genomic selection - 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