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. 2020 Oct 28;586(7831):741–748. doi: 10.1038/s41586-020-2859-7

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© The Author(s) 2020

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. 3 — a, Novel variants per individual in each population (n = 24 biological independent samples randomly chosen from each group to match the smallest used dataset). Shading within a population reflects self-identified ethnolinguistic affiliations (Supplementary Table 3). b, c, The number of additional total (b) and common (c) variants discovered in each population starting with those identified in BOT. d–g, Correlation (Pearson, line of best fit is shown in green) between the number of novel SNVs and proportion of KS in BOT (d), RFF in CAM (e), non-NC in MAL (f) and east African (EA) ancestry in BRN (g).