Publications

Inferring the genetic structure of populations at the subpopulation level is crucial for understanding the evolutionary forces and demographic histories that shape genetic diversity. Among the most … widely used approaches in population genetics are methods based on admixture and structure modeling—named after the respective software tools—which have become standard due to their intuitive, interpretable outputs. In this study, we address a key methodological question: how does traditional admixture-based decomposition of genetic components in multilocus population data relate to clustering approaches that leverage machine learning, specifically Self-Organizing Maps (SOMs)? We implemented this approach through our custom SOM-based tool, SOMmelier, which enables the portrayal of genetic structure by identifying modules of co-mutated SNPs and arranging them in a topology-aware genetic landscape. In this context, topology-awareness refers to the organization of genetic modules in a two-dimensional map, where their spatial proximity reflects mutual similarity. As a case study, we applied SOMmelier to investigate the population genetics of European grapevine. Based on prior literature, we considered up to six genetic components, which formed a genetic landscape that closely mirrors the geographic expanse of the classical Mediterranean world—from Western Asia through the Caucasus to Western Europe. The resulting topology reflects the dynamic spatial and temporal nature of grapevine domestication and diffusion. SOMmelier thus represents a complementary and powerful framework for genetic data analysis. By integrating the topological structure of SNP co-variation, it offers new perspectives on population structure, evolutionary history, and trait associations in grapevine—and has broader applicability to other species and systems in population genetics.