ShinyGS: A User-Friendly Toolkit for Genomic Selection
Author Information
Author(s): Yu Le, Dai Yifei, Zhu Mingjia, Guo Linjie, Ji Yan, Si Huan, Cheng Lirui, Zhao Tao, Zan Yanjun
Primary Institution: Tobacco Research Institute, Chinese Academy of Agricultural Sciences
Hypothesis
Can a graphical toolkit simplify the application of genomic selection methods for breeders without programming skills?
Conclusion
ShinyGS significantly simplifies genomic prediction applications, making advanced genomic selection methods more accessible to breeders.
Supporting Evidence
- ShinyGS includes 16 genomic prediction methods to enhance prediction performance.
- The toolkit is freely available to non-commercial users at Docker Hub.
- Benchmarking showed that MKRKHS and GBM models had the highest prediction accuracies for different traits.
Takeaway
ShinyGS is a tool that helps farmers and scientists predict how plants will grow using easy-to-use software, so they don't need to be computer experts.
Methodology
ShinyGS integrates multiple genomic selection algorithms and allows users to upload genotype and phenotype data for analysis.
Limitations
The application requires careful data preparation and may not perform well with very large datasets due to computational demands.
Digital Object Identifier (DOI)
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