Machine learning algorithms translate big data into predictive breeding accuracy

Sammanfattning

Statistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and environmental data. ML algorithms automatically identify relevant features and use cross-validation to ensure robust models and improve prediction reliability in new lines. Furthermore, ML analyses of genotype-by-environment (G×E) interactions can offer insights into the genetic factors that affect performance in specific environments. By leveraging historical breeding data, ML streamlines strategies and automates analyses to reveal genomic patterns. In this review we examine the transformative impact of big data, including multi-trait genomics, phenomics, and environmental covariables, on genomic-enabled prediction in plant breeding. We discuss how big data and ML are revolutionizing the field by enhancing prediction accuracy, deepening our understanding of G×E interactions, and optimizing breeding strategies through the analysis of extensive and diverse datasets.

Nyckelord

statistical machine learning; genomic prediction; big genomics; phenomics; environmental data; climate change; modern breeding programs

Publicerad i

Trends in Plant Science
2024

SLU författare

• Ortiz Rios, Rodomiro Octavio

  • Institutionen för växtförädling, Sveriges lantbruksuniversitet
    

Globala målen (SDG)

SDG2 Ingen hunger

UKÄ forskningsämne

Jordbruksvetenskap
Genetik och förädling
Växtbioteknologi

Publikationens identifierare

• DOI: https://doi.org/10.1016/j.tplants.2024.09.011

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/133057