A k-mer-based genome-wide association study approach empowering gene mining in polyploids

Genome-wide association studies in complex polyploids are hindered by genotyping ambiguity and allele dosage complexity. Here we present KMERIA, a k-mer-based framework specifically designed to address these challenges, enabling efficient genotyping and robust association mapping in complex polyploid genomes. Rigorous benchmarking with simulated and empirical datasets demonstrates that KMERIA surpasses existing methods in accuracy and statistical power. By applying KMERIA to 290 wild sugarcane (Saccharum spontaneum) accessions and integrating a 15-accession graph pangenome to capture structural variations, we identified new genes regulating sucrose biosynthesis (SsMGT) and tillering (for example, SsERF14, SsNGA5, SsNAC, SsARF8, SsLOG and SsSCR). These findings elucidate the genetic architecture of yield-related traits and provide actionable targets for sugarcane breeding. Collectively, KMERIA bridges a critical methodological gap in polyploid genomics, while our graph-pangenome integration provides a powerful framework for deciphering genotype–phenotype relationships in crops with complex architectures. KMERIA, a k-mer-based genome-wide association study approach, specifically designed for polyploids, enhances statistical power and efficiency in agronomic variant discovery when applied to high-ploidy species such as wild sugarcane Saccharum spontaneum.