Probabilistic ancestry maps: a method to assess and visualize population substructures in genetics

BMC Bioinformatics
Héléna A Gaspar, Gerome Breen


Principal component analysis (PCA) is a standard method to correct for population stratification in ancestry-specific genome-wide association studies (GWASs) and is used to cluster individuals by ancestry. Using the 1000 genomes project data, we examine how non-linear dimensionality reduction methods such as t-distributed stochastic neighbor embedding (t-SNE) or generative topographic mapping (GTM) can be used to provide improved ancestry maps by accounting for a higher percentage of explained variance in ancestry, and how they can help to estimate the number of principal components necessary to account for population stratification. GTM generates posterior probabilities of class membership which can be used to assess the probability of an individual to belong to a given population - as opposed to t-SNE, GTM can be used for both clustering and classification. PCA only partially identifies population clusters and does not separate most populations within a given continent, such as Japanese and Han Chinese in East Asia, or Mende and Yoruba in Africa. t-SNE and GTM, taking into account more data variance, can identify more fine-grained population clusters. GTM can be used to build probabilistic classification models, and is as eff...Continue Reading


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Aug 4, 2009·Genome Research·David H AlexanderKenneth Lange
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Jun 22, 2011·Nucleic Acids Research·Natascha BushatiChristopher Watkins
Nov 26, 2014·Journal of Chemical Information and Modeling·Héléna A GasparAlexandre Varnek
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Jun 14, 2016·Cell·UNKNOWN 1001 Genomes Consortium. Electronic address:, UNKNOWN 1001 Genomes Consortium
Jul 19, 2017·Journal of Bioinformatics and Computational Biology·Wentian LiHenry Han

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