Expanding genomic prediction in plant breeding : harnessing big data, machine learning, and advanced software
Crossa, J.
Expanding genomic prediction in plant breeding : harnessing big data, machine learning, and advanced software - United States of America : Elsevier Ltd., 2025.
Peer review Open access
With growing evidence that genomic selection (GS) improves genetic gains in plant breeding, it is timely to review the key factors that improve its efficiency. In this feature review, we focus on the statistical machine learning (ML) methods and software that are democratizing GS methodology. We outline the principles of genomic-enabled prediction and discuss how statistical ML tools enhance GS efficiency with big data. Additionally, we examine various statistical ML tools developed in recent years for predicting traits across continuous, binary, categorical, and count phenotypes. We highlight the unique advantages of deep learning (DL) models used in genomic prediction (GP). Finally, we review software developed to democratize the use of GP models and recent data management tools that support the adoption of GS methodology.
Text in English
1360-1385 1878-4372 (Online)
https://doi.org/10.1016/j.tplants.2024.12.009
Models
Plant breeding
Genomics
Forecasting
Software development
Marker-assisted selection
Statistical methods
Machine learning
Expanding genomic prediction in plant breeding : harnessing big data, machine learning, and advanced software - United States of America : Elsevier Ltd., 2025.
Peer review Open access
With growing evidence that genomic selection (GS) improves genetic gains in plant breeding, it is timely to review the key factors that improve its efficiency. In this feature review, we focus on the statistical machine learning (ML) methods and software that are democratizing GS methodology. We outline the principles of genomic-enabled prediction and discuss how statistical ML tools enhance GS efficiency with big data. Additionally, we examine various statistical ML tools developed in recent years for predicting traits across continuous, binary, categorical, and count phenotypes. We highlight the unique advantages of deep learning (DL) models used in genomic prediction (GP). Finally, we review software developed to democratize the use of GP models and recent data management tools that support the adoption of GS methodology.
Text in English
1360-1385 1878-4372 (Online)
https://doi.org/10.1016/j.tplants.2024.12.009
Models
Plant breeding
Genomics
Forecasting
Software development
Marker-assisted selection
Statistical methods
Machine learning