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

Zhu, Mingjia ; Zheng, Zeyu ; Liu, Wei ; Han, Yu ; Mu, Wenjie (Universidad de Zaragoza) ; Yin, Tongming ; Dai, Xiaogang ; Wu, Huaitong ; Yang, Yongzhi ; Zan, Yanjun ; Liu, Jianquan

Resumen: 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.

Idioma: Inglés

DOI: 10.1093/hr/uhag035

Año: 2026

Publicado en: HORTICULTURE RESEARCH 13, 5 (2026), [7 pp.]
ISSN: 2662-6810

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Botánica (Dpto. CC.Agrar.y Medio Natural)