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Related Experiment Video

Updated: May 24, 2025

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

Published on: June 16, 2018

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

Kunhui He^1,2, Tingxi Yu^1,2, Shang Gao^1,2

¹State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), CIMMYT-China Office, Beijing, 100081, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|March 6, 2025

Summary

This study developed an automated machine learning framework integrating environmental and genomic data to improve maize breeding. Incorporating environmental parameters and trait-associated markers enhanced genomic prediction accuracy for climate-adaptive varieties.

Keywords:

environmental data genetic analysis genomic selection genotype‐by‐environment interactions machine learning

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Last Updated: May 24, 2025

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

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Published on: June 16, 2018

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Published on: June 23, 2023

Area of Science:

Plant genetics and breeding
Agricultural science
Computational biology

Background:

Genotype, environment, and genotype-by-environment (G×E) interactions significantly influence crop phenotypes.
Accurate genetic analyses and genomic predictions are crucial for developing improved crop varieties, especially under changing environmental conditions.

Purpose of the Study:

To construct and validate an automated machine learning framework integrating environmental and genomic data for enhanced maize genetic analyses and genomic predictions.
To identify genetic markers associated with phenotypic plasticity and environmental stability.
To assess the impact of environmental factors on crop phenotypes.

Main Methods:

Utilized a large-scale, multi-environment hybrid maize dataset.
Developed dimensionality-reduced environmental parameters (RD_EPs) aligned with developmental stages.
Performed genome-wide association studies to identify trait-associated markers (TAMs) for phenotypic plasticity (PP-TAMs), environmental stability (Main-TAMs), and G×E interactions (G×E-TAMs).
Trained genomic prediction models incorporating TAMs and RD_EPs.

Main Results:

Identified 539 PP-TAMs, 223 Main-TAMs, and 92 G×E-TAMs, indicating distinct genetic underpinnings for phenotypic plasticity and G×E interactions.
Established linear relationships between RD_EPs and traits, quantifying environmental influence on phenotype.
Genomic prediction models trained with TAMs and RD_EPs showed a 14.02% to 28.42% increase in prediction accuracy compared to genome-wide marker approaches.

Conclusions:

Environmental data integration significantly improves genetic analysis and genomic selection accuracy in maize.
The developed machine learning framework offers a scalable approach for identifying climate-adaptive maize varieties.
Understanding G×E interactions is key to breeding resilient crops for diverse environments.