KLFDAPC: a supervised machine learning approach for spatial genetic structure analysis.

ABSTRACT: Geographic patterns of human genetic variation provide important insights into human evolution and disease. A commonly used tool to detect and describe them is principal component analysis (PCA) or the supervised linear discriminant analysis of principal components (DAPC). However, genetic features produced from both approaches could fail to correctly characterize population structure for complex scenarios involving admixture. In this study, we introduce Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC), a supervised non-linear approach for inferring individual geographic genetic structure that could rectify the limitations of these approaches by preserving the multimodal space of samples. We tested the power of KLFDAPC to infer population structure and to predict

SUBMITTER: Qin X 

PROVIDER: S-EPMC9294434 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature