DA: Population Structure Inference Using Discriminant Analysis

Genetic variations in a species across geographic areas typically exhibit spatial clines. There is increasing interest inferring population genetic structure to understand the patterns of variation and evolution species. Here, we present da package propose infer using discriminant analysis (DA). We incorporate five supervised learning approaches (DAPC, LDAKPC, LFDA, LFDAKPC KLFDA) into within same DA family, but with different linear nonlinear properties. tested performance properties these for inference both simulated empirical data. Results showed that preserved global under each scenario. Notably, features produced from KLFDA LFDA had higher correlations isolation-by-distance model discriminatory power identification, achieving best performance. The applications data indicated all methods could intuitively capture continuous gradients while discriminate nuanced structures other cannot. These can be applied statistical inferences genetics beyond. available at https://cran.r-project.org/web/packages/DA/index.html. recommend users choosing appropriately depending on their scientific questions target