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. 2022 Jul 4;54:49. doi: 10.1186/s12711-022-00740-8

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© The Author(s) 2022

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

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Fig. 4 — Accuracy of imputation from medium-density to sequence level. Imputation was performed using a reference panel of 162 horses (RP2) and the software Beagle 5.1. a Accuracy of imputation per chromosome. b Accuracy of imputation against marker density of the medium-density SNP array per chromosome. The dashed line indicates a linear regression model of y = 0.017x + 0.232 (adjusted R-squared: 0.669, p-value: 1.2 × 10^–8). c Accuracy of imputation against marker density of the medium-density SNP array for the ECA31 chromosome. The dashed line indicates a linear regression model of y = 0.017 + 0.233 (adjusted R-squared: 0.896, p-value: 1.6 × 10^–13). d Accuracy of imputation plotted against the position on ECA31, with positions of the medium-density (MD) SNP array shown in orange