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. 2017 Jan 4;49:2. doi: 10.1186/s12711-016-0279-9

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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. 3 — Prediction accuracies by cross-validation for a variety of methods applied to backfat (BFT), carcass weight (CWT), eye-muscle area (EMA) and marbling (MAR). Conventional PBLUP based on only genotyped individuals (PBLUP-G) or using all animals (PBLUP), BayesB with chosen π (BAYESC(π = chosen value)), BayesC with chosen π (BAYESC (π = chosen value)) BayesC with π = 0 (BAYESC (π = 0)) or BayesC estimating π (BAYESC (π ESTIMATION)), single-step genomic BLUP constructing two different genomic relationship matrix (SSGBLUP-I and SSGBLUP-II) and single-step Bayesian regression corresponding to Bayesian methods (SSBR-B (π = chosen value), SSBR-C (π = chosen value), SSBR-C (π = 0), and SSBR-C (π ESTIMATION))