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. 2019 May 28;20:106. doi: 10.1186/s13059-019-1697-0

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© The Author(s). 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 1 — Flowchart of the WhoGEM prediction machine. The inputs (left) include data preprocessing, typically LD-pruned SNPs, the ProvenancePredictor algorithm using geographical locations, and phenotypic characterization. The WhoGEM prediction machine (center) uses geographical information as covariates to help in resolving the number of admixture components. The phenotypic characterization allows predicting quantitative traits using admixture components of individuals. The outputs (right) are covariate-informed population structure and n-dimensional admixture vectors, putative localization of unknown samples, and prediction of quantitative traits using admixture proportions