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TECHNOLOGY AND CODE article

Front. Plant Sci.
Sec. Plant Bioinformatics
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1480902

ShinyGS-A graphical toolkit with a serial of genetic and machine learning models for genomic selection: application, benchmarking and recommendations

Provisionally accepted
Le Yu Le Yu 1,2Yifei Dai Yifei Dai 3Mingjia Zhu Mingjia Zhu 4Linjie Guo Linjie Guo 1Yan Ji Yan Ji 1SI HUAN SI HUAN 1Lirui Cheng Lirui Cheng 1Tao Zhao Tao Zhao 5*Yanjun Zan Yanjun Zan 1*
  • 1 Tobacco Research Institute, Chinese Academy of Agricultural Sciences, Qingdao, China
  • 2 Department of Plant Biology, Faculty of Natural Resources and Agricultural Sciences, Swedish University of Agricultural Sciences, Uppsala, Uppsala, Sweden
  • 3 Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, United States
  • 4 College of Ecology, Lanzhou University, Lanzhou, Gansu Province, China
  • 5 College of Horticulture, Northwest Agriculture and Forestry University, Yangling, China

The final, formatted version of the article will be published soon.

    Genomic prediction is a powerful approach for improving genetic gain and shorten 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, application of these models requires advanced R programming skills and command-line tools to perform quality control, formatting input files, installing 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 population and genetic architecture. Recommendations were given for specific breeding application. 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/yfd2/ags.

    Keywords: Genomic prediction, BLUP, machine learning, Breeding, Graphical toolkit

    Received: 14 Aug 2024; Accepted: 02 Dec 2024.

    Copyright: © 2024 Yu, Dai, Zhu, Guo, Ji, HUAN, 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) or licensor 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.

    * Correspondence:
    Tao Zhao, College of Horticulture, Northwest Agriculture and Forestry University, Yangling, China
    Yanjun Zan, Tobacco Research Institute, Chinese Academy of Agricultural Sciences, Qingdao, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.