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. 2017 Dec 5;49:89. doi: 10.1186/s12711-017-0364-8

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© The Author(s) 2017

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. 4 — Reliability of prediction using various proportions of genomic segments. Predictions were based on post-Gibbs analyses of samples from the MT-100-BayesA model. Segments were ranked based on explained covariance separately for each training set