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. 2021 Aug 25;12:5118. doi: 10.1038/s41467-021-25435-4

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© The Author(s) 2021

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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Fig. 6 — Four tagging SNVs were first identified by perfectly separating super haplogroups H4 and H5 among the seven BAC haplogroups and confirmed based on patterns of linkage disequilibrium (LD) in samples from the HGDP panel. a, b Distributions of super haplogroups H4 (red, 57.1%) and H5 (blue, 40.3%) and their diploid type in the HGDP populations across the world. Other haplotypes were found in ~2.6% of the samples. Note that archaic hominin haplotypes from the Neanderthal (n = 3) and Denisovan (n = 1) samples all carry H4 haplotypes, and as a representation the geographic location of these samples was placed in the Altai Mountains in Siberia (arrows). These maps were generated using the R packages rgdal (v1.5), scatterpie (v0.1.6), and Natural Earth. c Multi-tissue eQTL plots show consistent patterns of associations between the four tagging SNVs and expression levels of TCAF2 across 50 tissues. Effect sizes were calculated as the effect of the alternative allele (blue) relative to the reference allele (read) as defined in GTEx (release v8) and are scaled using color. The (unadjusted) p values of eQTL association are computed by testing the alternative hypothesis that the slope of a linear regression model between genotype and expression deviates from 0 and represented by dot sizes. Note that crosses (×) indicate insignificant associations.