Translating functional molecular knowledge into crop-breeding success.

Sequence-based deep learning predicts genetic variant effects at base-pair resolution to enhance crop breeding strategies.

• Why it matters: Traditional plant breeding is limited by evolutionary constraints, slowing the pace of crop improvement; functional molecular knowledge can overcome these barriers.
• What they did: The approach uses high-quality genome sequence data and deep learning to link variant effect predictions to agronomically important traits, enabling targeted breeding applications.
• The result: This method may facilitate gene introgression, removal of deleterious mutations, and design of new plant ideotypes, supporting future crop improvement under changing environments.