Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials.

ABSTRACT:

Key message

Incorporating feature-engineered environmental data into machine learning-based genomic prediction models is an efficient approach to indirectly model genotype-by-environment interactions. Complementing phenotypic traits and molecular markers with high-dimensional data such as climate and soil information is becoming a common practice in breeding programs. This study explored new ways to combine non-genetic information in genomic prediction models using machine learning. Using the multi-environment trial data from the Genomes To Fields initiative, different models to predict maize grain yield were adjusted using various inputs: genetic, environmental, or a combination of both, either in an additive (genetic-and-environmental; G+E) or a multiplicative (genotype-by-enviro

SUBMITTER: Fernandes IK 

PROVIDER: S-EPMC11266441 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature