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Article

Leveraging Automated Machine Learning for Environmental Data-Driven Genetic Analysis and Genomic Prediction in Maize Hybrids.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) - 1 May 2025

He Kunhui, Yu Tingxi, Gao Shang, Chen Shoukun, Li Liang, Zhang Xuecai, Huang Changling, Xu Yunbi, Wang Jiankang, Prasanna Boddupalli M, Hearne Sarah, Li Xinhai, Li Huihui

Abstract excerpt

Genotype, environment, and genotype-by-environment (G×E) interactions play a critical role in shaping crop phenotypes. Here, a large-scale, multi-environment hybrid maize dataset is used to construct and validate an automated machine learning framework that integrates environmental and genomic data for improved accuracy and efficiency in genetic analyses and genomic predictions. Dimensionality-reduced...

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Topics

Zea mays
Machine Learning
Genome-Wide Association Study
Phenotype
Gene-Environment Interaction

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Genomics

Genotype

Environment

Genome, Plant

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Identifiers

PMID: 40047344
DOI: 10.1002/advs.202412423
PMCID: PMC12061318

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