Integrating deep learning and pangenomics to recover missing heritability from wild structural variations.

Crop domestication has induced a severe genetic bottleneck that reduces the adaptive diversity present in modern cultivars. Standard intra-population genomic prediction models reliant on linear reference genomes and SNPs fail to capture the full spectrum of phenotypic variance hidden in wild relatives. Realizing this potential requires broadening the predictive paradigm from selection within narrow breeding populations toward evolutionary-scale inference across the entire wild-to-cultivated continuum. This missing heritability is largely sequestered within complex structural variations such as presence-absence and copy number variants. These variations drive environmental adaptations but remain obscured by reference bias. To recover these unmapped structural variations the field is evolving from linear coordinates to high-dimensional genomic data representations. We review this transition by contrasting explicit graph topologies that map reticulate evolution with implicit encodings like K-mers that capture sequence composition independent of alignment. Processing these complex and high-dimensional features necessitates advanced computational tools. We synthesize emerging deep learning frameworks and highlight how Graph Neural Networks resolve inheritance paths in topological data while Transformer-based foundation models extract functional syntax from sequence context. These architectures effectively integrate structural variations to resolve non-additive effects such as epistasis missed by traditional models. Computing these hidden structural variations facilitates the precise utilization of wild germplasm. We demonstrate how AI-driven strategies enable zero-shot prediction for uncharacterized wild alleles and optimize genotype-by-environment interactions. Ultimately these approaches pave the way for accelerated de novo domestication of climate-resilient crops.