Explainable AI identifies recombination and chromatin environment as key predictors of subgenome evolution in maize and Brassica

Abstract

Polyploidy, or whole genome duplication, reshapes genomes through biased gene loss and regulatory rewiring, yet the drivers of biased fractionation among subgenomes remain unclear. Using maize and Brassica rapa as model allopolyploids, we compiled 60 genomic and epigenomic features in maize and 45 in B. rapa and constructed supervised machine-learning models to classify genes by subgenome identity. In both species, eXplainable Artificial Intelligence (XAI) approaches identified recombination rate as the most influential and highly interconnected feature despite nonsignificant mean differences between subgenome groups. Chromosome location, transposon density, and chromatin-associated features, including the proximity of accessible chromatin regions to genes and active histone marks such as H3K9ac and H3K27ac, consistently ranked among the top contributors to subgenome classification. XAI-derived co-variation and interaction networks further revealed recombination rate as the central node connecting significant features. Together, these results highlight recombination and chromatin environment as major predictors of subgenome divergence in polyploid genomes. TeaserRecombination and chromatin environment are key predictors of unequal gene loss and subgenome dominance after polyploidy.