Biology-driven genebank pangenetics meet AI-powered predictions to transform crop breeding

The modern era of crop improvement is characterized by two distinct paths: (i) biology-driven molecular genetics focused on dissecting the mechanisms underlying complex traits by a reductionist approach and (ii) data-driven quantitative genetics, which prioritizes accurate prediction of final trait performance, such as yield, without necessarily clarifying the underlying biological mechanisms. These approaches, one rooted in functional discovery and the other in statistical association, have each made significant strides, yet with limited overlap. However, their conjunction represents an emerging opportunity; integrating the functional insights from pangenetics, encompassing causal variants and haplotype diversity into artificial intelligence (AI)-based predictive frameworks, can offer a more holistic perspective for customizing and testing crop genomic ideotypes for specific agroecologies, dynamic market demands, and increasing climate uncertainties.