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. 2022 Sep 24;54:65. doi: 10.1186/s12711-022-00756-0

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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. 2 — Genomic prediction accuracy with the marker array (Chip) or with preselected WGS data (Top40k, ChipPlusSign, and ChipPlusSign*) with varying training set sizes for the ADG, BFT, and LD traits in the three largest lines. In ChipPlusSign*, variants are preselected based on associations tested using the largest training set available for each trait and line