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Background and Environment Affect Phenotype

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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
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Related Experiment Video

Updated: Aug 12, 2025

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

Crop genomic selection with deep learning and environmental data: A survey.

Sheikh Jubair¹, Mike Domaratzki²

¹Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada.

Frontiers in Artificial Intelligence

|January 27, 2023

Summary

Deep learning models now predict crop phenotypes across diverse environments by integrating genomic data with environmental factors like temperature and soil conditions. This advances genomic selection for complex traits under varying conditions.

Keywords:

GxE MET deep learning environment genomic selection machine learning

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Published on: March 1, 2024

Area of Science:

Agricultural Science
Genomics
Machine Learning

Background:

Machine learning for crop genomic selection in single environments is established.
Predicting crop phenotypes across multiple environments, especially using deep learning, is an emerging field.
Genotype-by-environment (GxE) interactions are crucial for multi-environment prediction.

Purpose of the Study:

To review emerging deep learning techniques for genomic selection.
To highlight the integration of environmental data into crop phenotype prediction models.
To address challenges in modeling GxE interactions.

Main Methods:

Review of recent deep learning approaches for genomic selection.
Focus on models incorporating heterogeneous environmental data (temperature, soil, precipitation).
Discussion of models for multi-environment prediction.

Main Results:

Deep learning models show promise for predicting crop phenotypes across diverse environments.
Integration of environmental data improves prediction accuracy for GxE.
Heterogeneous data sources are effectively utilized by advanced models.

Conclusions:

Emerging deep learning techniques offer powerful tools for crop genomic selection under varying environmental conditions.
Incorporating environmental data directly into models is key for accurate GxE prediction.
Future research should focus on refining these deep learning approaches for agricultural applications.