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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 individual geographic origin using neural networks. Simulation results showed that KLFDAPC has higher discriminatory power than PCA and DAPC. The application of our method to empirical European and East Asian genome-wide genetic datasets indicated that the first two reduced features of KLFDAPC correctly recapitulated the geography of individuals and significantly improved the accuracy of predicting individual geographic origin when compared to PCA and DAPC. Therefore, KLFDAPC can be useful for geographic ancestry inference, design of genome scans and correction for spatial stratification in GWAS that link genes to adaptation or disease susceptibility.<\/jats:p>","DOI":"10.1093\/bib\/bbac202","type":"journal-article","created":{"date-parts":[[2022,5,7]],"date-time":"2022-05-07T07:08:34Z","timestamp":1651907314000},"source":"Crossref","is-referenced-by-count":4,"title":["KLFDAPC: a supervised machine learning approach for spatial genetic structure analysis"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2351-3610","authenticated-orcid":false,"given":"Xinghu","family":"Qin","sequence":"first","affiliation":[{"name":"Centre for Biological Diversity, Sir Harold Mitchell Building, University of St Andrews , Fife, KY16 9TF , UK"}]},{"given":"Charleston W K","family":"Chiang","sequence":"additional","affiliation":[{"name":"Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine & Department of Quantitative and Computational Biology, University of Southern California , USA"}]},{"given":"Oscar E","family":"Gaggiotti","sequence":"additional","affiliation":[{"name":"Centre for Biological Diversity, Sir Harold Mitchell Building, University of St Andrews , Fife, KY16 9TF , UK"}]}],"member":"286","published-online":{"date-parts":[[2022,6,2]]},"reference":[{"key":"2022071906085362400_ref1","volume-title":"Evolution in health and disease","author":"Barbujani","year":"1999"},{"key":"2022071906085362400_ref2","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1007\/s00439-005-0039-3","article-title":"Geography is a better determinant of human genetic differentiation than ethnicity","volume":"118","author":"Manica","year":"2005","journal-title":"Hum Genet"},{"key":"2022071906085362400_ref3","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/09581590500048192","article-title":"Beyond the divides: towards critical population health 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