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. 2022 Sep 21;13:99. doi: 10.1186/s40104-022-00751-x

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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 — Teat number relevant traits-specific selection signature detection. A Detection of traits-specific selection signatures based on the constructed population pairs with extreme differences in total teat number. The Y-axis displayed the θ_π ratio and the X-axis showed the F_ST value. The 1% of windows with the highest θ_π ratio and F_ST values was considered the potential selection regions. B QTL enrichment analyses with teat number relevant traits-specific selection signature detection. The richness factor was obtained by the ratio of the number of QTLs annotated in the candidate regions and the total number of each QTL