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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. 1 — Comparison of the four methods (i.e. Lasso, ENET, LMM and BSLMM) for predicting gene expression in scenarios I-III. a The results of scenario I where the BSLMM modeling assumption is satisfied and 15 causal SNPs are included in the sparse part. b The results of scenario II where the LMM modeling assumption is satisfied. c The results of scenario III where the sparse modeling assumption is satisfied and there are only 15 causal SNPs and the rest are all neutral. The performance is measured by R ². In each panel from left to right it corresponds to PVE = 0.1, 0.3 or 0.5 respectively