A topological map of the genetic components of grapevine-Admixture meets SOMmelier machine learning.

ABSTRACT: Inferring the genetic structure at the subpopulation level is crucial for understanding the demographic histories that shape genetic diversity. Among the most widely used approaches 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 the 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 arrangin

SUBMITTER: Baloyan A 

PROVIDER: S-EPMC12948125 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature