Multi-trait/environment sparse genomic prediction using the SFSI R-package
Marco Lopez-Cruz1,2, Gustavo de Los Campos1,2,3
1Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan, USA.
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Summary
Sparse selection indices (SSIs) and sparse genomic prediction (SGP) are combined into a multi-trait/environment SGP (MT-SGP) framework. This approach improves prediction accuracy for genetic merit, outperforming traditional methods in crop breeding.
Area of Science:
- Quantitative genetics
- Plant breeding
- Bioinformatics
Background:
- Sparse selection indices (SSIs) predict genetic merit using high-dimensional phenotypes.
- Sparse genomic prediction (SGP) predicts genetic merit using subsets of training data.
Purpose of the Study:
- Introduce a novel framework for multi-trait/environment sparse genomic prediction (MT-SGP).
- Combine features of SSI and SGP into a unified model for enhanced genetic merit prediction.
Main Methods:
- Developed an MT-SGP framework integrating SSI and SGP methodologies.
- Created an R-package (sparse family and selection index) for solving SSI, SGP, and MT-SGP problems.
- Conducted extensive benchmarks using three crop datasets across 30 traits/environments.
Main Results:
- MT-SGP demonstrated comparable or superior prediction accuracy (up to 15% gain) compared to MT-genomic best linear unbiased prediction.
- Identified conditions (sample size, genetic correlation, heritability) favoring MT-SGP's performance gains.
- The R-package provides practical tools for implementing these advanced prediction methods.
Conclusions:
- MT-SGP offers a powerful alternative for genetic merit prediction in complex breeding programs.
- The framework effectively borrows information from correlated traits and genetically similar individuals.
- The developed R-package facilitates the application of MT-SGP in quantitative genetics research and breeding practice.