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. 2019 Jan 9;9:11. doi: 10.1038/s41598-018-36673-w

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Clustering for dry matter intake. Hierarchical clustering for feed intake levels (top) within feed efficiency group, low (left) or high (right), based on selected contigs from metaGWAS analyses for dry matter intake. Bootstrap (bp) and approximately unbiased (au) values were generated using Pvclust with 1000 iterations (bottom). (each individual is assigned to any of the following groups: L-Ldmi = low feed efficiency and low dry matter intake; L-Hdmi = low feed efficiency and high dry matter intake; H-Ldmi = high feed efficiency and low dry matter intake; H-Hdmi = high feed efficiency and high dry matter intake).