Machine learning for predicting phenotype from genotype and environment

Predicting phenotype with genomic and environmental information is critically needed and challenging. Machine learning methods have emerged as powerful tools to make accurate predictions from large and complex biological data. Here, we review the progress of phenotype prediction models enabled or improved by machine learning methods. We categorized the applications into three scenarios: prediction with genotypic information, with environmental information, and with both. In each scenario, we illustrate the practicality of prediction models, the advantages of machine learning, and the challenges of modeling complex relationships. We discuss the promising potential of leveraging machine learning and genetics theories to develop models that can predict phenotype and also interpret the biological consequences of changes in genotype and environment.

Automated summary

Predicting phenotype with genomic and environmental information using machine learning methods is challenging but necessary. This review discusses the progress of phenotype prediction models enabled or improved by machine learning. The applications are categorized into scenarios based on genotypic information, environmental information, and both, highlighting the practicality, advantages, and challenges of modeling complex relationships. The potential of leveraging machine learning and genetics theories to develop models that predict phenotype and interpret biological consequences is also discussed.