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dc.contributor.authorQin, Xinghu
dc.contributor.authorChiang, Charleston W K
dc.contributor.authorGaggiotti, Oscar E
dc.identifier.citationQin , X , Chiang , C W K & Gaggiotti , O E 2022 , ' KLFDAPC : a supervised machine learning approach for spatial genetic structure analysis ' , Briefings in Bioinformatics , vol. 23 , no. 4 , bbac202 .
dc.identifier.otherPURE: 280015769
dc.identifier.otherPURE UUID: 37f47524-f34c-48d1-8d65-b6931aeced17
dc.identifier.otherPubMed: 35649387
dc.identifier.otherORCID: /0000-0003-1827-1493/work/114336023
dc.identifier.otherScopus: 85134632664
dc.identifier.otherWOS: 000804257200001
dc.descriptionFunding: CSC-University of St Andrews Joint Scholarship (to X.Q.); International Postdoctoral Exchange Fellowship Program (Talent-Introduction Program) from China Postdoc Council (to X.Q.); National Institute of General Medical Sciences (NIGMS) of the National Institute of Health (grant R35GM142783 to C.W.K.C.). Part of the computation for this work is supported by USC’s Center for Advanced Research Computing (
dc.description.abstractGeographic 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 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.
dc.relation.ispartofBriefings in Bioinformaticsen
dc.rightsCopyright © The Author(s) 2022. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License.(, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.subjectMachine learningen
dc.subjectPopulation structureen
dc.subjectIndividual geographic originen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.titleKLFDAPC : a supervised machine learning approach for spatial genetic structure analysisen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Centre for Biological Diversityen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. St Andrews Bioinformatics Uniten
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.description.statusPeer revieweden

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