Bioinformatic Framework Reconstructs Evolutionary History of Complex Polyploid Genomes

Whole-genome duplication has repeatedly reshaped plant genomes and driven evolutionary innovation, ecological adaptation, and crop diversification. In allopolyploid species, chromosomes originate from different ancestral genomes, forming multiple subgenomes that diverge and interact over time. Identifying these subgenomes is essential for understanding genome evolution, yet traditional methods rely heavily on known diploid progenitors, which are often extinct or unknown. Transposable elements, especially long terminal repeat retrotransposons, accumulate in lineage-specific patterns and retain molecular traces of past evolutionary events. However, robust frameworks for translating these patterns into reliable subgenome assignments have broad gaps. Based on these challenges, there is a need to develop new strategies to reconstruct polyploid genome evolution in the absence of known progenitor genomes.

Researchers from the U.S. Department of Agriculture and collaborating institutions reported a new bioinformatic framework in Horticulture Research, published (DOI: 10.1093/hr/uhaf132) on May 21, 2025, that reconstructs the evolutionary history of complex polyploid genomes. Using a serial similarity matrix approach based on long terminal repeat retrotransposons, the team reassessed the genome of cultivated octoploid strawberry (Fragaria × ananassa). Their analysis clarifies subgenome structure and identifies multiple ancient genome-merging events that shaped the modern strawberry, resolving long-standing debates about its evolutionary origin.

The researchers developed a method that tracks genome evolution through three conceptual phases: before progenitor species diverged, during their independent evolution, and after genome merger. Long terminal repeat retrotransposons proliferating during the divergence phase retain subgenome-specific signatures. By calculating similarity matrices of these elements across chromosomes and examining clustering patterns at different similarity thresholds, the team created a "serial similarity matrix" that captures evolutionary signals across time.

The method was first validated in well-characterized allopolyploid crops, including teff and cotton, where it correctly separated known subgenomes and distinguished pre- and post-polyploidization events. It was also tested on artificially constructed polyploid genomes, confirming its sensitivity to divergence time and transposon abundance.

Applied to octoploid strawberry, the approach identified four distinct subgenomes and revealed three sequential allopolyploidization events occurring between approximately 3.1–4.2, 1.9–3.1, and 0.8–1.9 million years ago. The analysis supports close relationships between two strawberry subgenomes and Fragaria vesca and Fragaria iinumae, while challenging earlier models that proposed additional diploid progenitors. The results indicate that extinct or unsampled relatives likely contributed to strawberry genome formation, highlighting the complexity of polyploid evolution.

"This work demonstrates how transposable elements can function as evolutionary time stamps embedded in plant genomes," said one of the study's senior authors. "By focusing on when and where these elements expanded, we can reconstruct genome history even when direct ancestral references are missing. This method provides a powerful new lens for studying polyploid crops and moves beyond reliance on incomplete progenitor data, offering a more objective and reproducible framework for evolutionary genomics."

Beyond strawberry, this approach has broad implications for crop genomics and plant breeding. Many agriculturally important species-including wheat, cotton, and sugarcane-are polyploids with complex evolutionary histories. Accurate subgenome resolution can improve gene annotation, trait mapping, and comparative genomics, ultimately supporting precision breeding and crop improvement. By enabling reconstruction of genome evolution without known ancestors, the serial similarity matrix method expands the toolkit for studying biodiversity, speciation, and adaptation. It also provides a transferable framework for investigating other complex polyploid organisms, helping bridge evolutionary biology and applied agricultural science.