Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software

Sammanfattning

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.

Publicerad i

Trends in Plant Science
2025

SLU författare

• Ortiz Rios, Rodomiro Octavio

  • Institutionen för växtförädling, Sveriges lantbruksuniversitet
    

Globala målen (SDG)

SDG2 Ingen hunger

UKÄ forskningsämne

Genetik och förädling inom lantbruksvetenskap

Publikationens identifierare

• DOI: https://doi.org/10.1016/j.tplants.2024.12.009

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/140437