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. 2017 May 11;18:368. doi: 10.1186/s12864-017-3759-6

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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. 2 — Comparison of the prediction performance of the four methods (i.e. Lasso, ENET, LMM and BSLMM) for the Geuvadis data. In each panel it lists the number of genes where BSLMM performs better and the number of genes where BSLMM performs worse; in the top (a)-(c), these numbers are computed across all the genes, and in the bottom (d)-(f) these numbers are computed across only the genes with R ² ≥ 0.05