Publications

Armenia is an important country of origin of cultivated Vitis vinifera subsp. vinifera and wild Vitis vinifera subsp. sylvestris and has played a key role in the long history of grape cultivation in … the Southern Caucasus. The existence of immense grapevine biodiversity in a small territory is strongly linked with unique relief and diverse climate conditions assembled with millennium-lasting cultural and historical context. In the present in-depth study using 25 nSSR markers, 492 samples collected in old vineyards, home gardens, and private collections were genotyped. For verification of cultivar identity, the symbiotic approach combining genotypic and phenotypic characterization for each genotype was carried out. The study provided 221 unique varieties, including 5 mutants, from which 66 were widely grown, neglected or minor autochthonous grapevine varieties, 49 turned out to be new bred cultivars created within the national breeding programs mainly during Soviet Era and 34 were non-Armenian varieties with different countries of origin. No references and corresponding genetic profiles existed for 67 genotypes. Parentage analysis was performed inferring 62 trios with 53 out of them having not been previously reported and 185 half-kinships. Instability of grapevine cultivars was detected, showing allelic variants, with three and in rare cases four alleles at one loci. Obtained results have great importance and revealed that Armenia conserved an extensive grape genetic diversity despite geographical isolation and low material exchange. This gene pool richness represents a huge reservoir of under-explored genetic diversity.

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.