MMGS: a novel genomic prediction framework to integrate genotype, environment and their interactions for multi-environment breeding trials

Abstract

Accurately predicting the performance of trees and crops across diverse and changing climates is essential for matching genotypes to both current and future environments. Yet modelling the complex interplay among genotype, environment, and phenotype in multi-environment trials remains a major challenge. Here, we introduce a unified framework, polygenic environmental interaction (PEI), directly models genotype-by-environment interactions through integrating genotypes and environmental covariates. We implemented an ensemble of 15 estimators spanning parametric, non-parametric, and machine-learning approaches. We then benchmarked our framework against the classical reaction norm (RN) using three genetically distinct populations and three traits with variable genetic architectures. Furthermore, we released an open-source R package, Multiple-environments genomic selection (MMGS), on GitHub. Together, our study offers a flexible and computationally efficient approach for multi-environment genomic prediction, enhancing breeding efficiency, providing deeper insights into modelling the genotype-environment-phenotype continuum.

Acknowledgements

This research was funded by National Key Research and Development Program of China (2021YFD2200202),the State Key Research & Development Project-Youth Scientist program (2023YFD1202400), National Science Foundation of China (32200503), Taishan Young Scholar Program and Distinguished Overseas Young Talents Program from Shandong province (2024HWYQ-079).

Author contributions

J.L. and Y.Z. conceived and supervised this project and its components. Y.H., W.L., Z.Z., and M.Z. collected raw datasets from open-source websites. Y.H., W.L., and M.Z. conducted the experiments. X.D. and H.W. built the hybrid willow populations. W.M., M.Z., and Y.Z. performed the development of this R package and M.Z. presented the data analysis. The plant locations: PZ (pengzhou), LS(leshan),YB(yibing),werechosenbyJ.L.M.Z.,J.L.,andY.Z.wrote the manuscript.

Data availability

The test datasets, including genotypic, phenotypic, and environmental data, can be accessed at this GitHub repository (https://github.com/Ryougi-yukiro/MMGS-bench). Additionally, the benchmark codes have been uploaded to this repository. For quick installation of the developed R package, kindly refer to the GitHub repository: https://github.com/Ryougi-yukiro/MMGS. This repository not only facilitates the installation of the R package but also provides example codes tailored for quick start. Detailed instructions can be found in the R documents and accompanying tutorial (https://multiplemethodgs.gitbook.io/MMGS_tutorial _v1).

Conflicts of interest statement

No conflict of interest was declared.

Supplementary material

Supplementary material is available at Horticulture Research online.

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