Abstract
In European and many African, Middle Eastern and southern Asian populations, lactase persistence (LP) is the most strongly selected monogenic trait to have evolved over the past 10,000 years1. Although the selection of LP and the consumption of prehistoric milk must be linked, considerable uncertainty remains concerning their spatiotemporal configuration and specific interactions2,3. Here we provide detailed distributions of milk exploitation across Europe over the past 9,000 years using around 7,000 pottery fat residues from more than 550 archaeological sites. European milk use was widespread from the Neolithic period onwards but varied spatially and temporally in intensity. Notably, LP selection varying with levels of prehistoric milk exploitation is no better at explaining LP allele frequency trajectories than uniform selection since the Neolithic period. In the UK Biobank4,5 cohort of 500,000 contemporary Europeans, LP genotype was only weakly associated with milk consumption and did not show consistent associations with improved fitness or health indicators. This suggests that other reasons for the beneficial effects of LP should be considered for its rapid frequency increase. We propose that lactase non-persistent individuals consumed milk when it became available but, under conditions of famine and/or increased pathogen exposure, this was disadvantageous, driving LP selection in prehistoric Europe. Comparison of model likelihoods indicates that population fluctuations, settlement density and wild animal exploitation—proxies for these drivers—provide better explanations of LP selection than the extent of milk exploitation. These findings offer new perspectives on prehistoric milk exploitation and LP evolution.
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Data availability
Data for running the aDNA analyses are available from https://github.com/ydiekmann/Evershed_Nature_2022. KML files, a summary of archaeological milk residue data, ecological proxy variables and a summary of radiocarbon dates are available from https://github.com/AdrianTimpson/2020-03-03523A. UK Biobank data are available from: https://www.ukbiobank.ac.uk/.
Code availability
R code for running the aDNA analyses is available from https://github.com/ydiekmann/Evershed_Nature_2022. Open-source R Code for running the UK Biobank analyses under MIT license are available from https://github.com/MRCIEU/lp-coevolution. R code for the generation of Figs. 1, 2, 3 and Extended Data Fig. 1 are available from https://github.com/AdrianTimpson/2020-03-03523A.
Change history
28 July 2022
The online PDF was replaced as the last two authors were deleted in error in the original PDF.
30 August 2022
A Correction to this paper has been published: https://doi.org/10.1038/s41586-022-05227-6
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Acknowledgements
This study was funded by the European Research Council (ERC) Advanced Grant ‘NeoMilk’ FP7-IDEAS-ERC/324202. M.R.-S. thanks the Royal Society for funding her Dorothy Hodgkin Fellowship (DHF\R1\180064 and RGF\EA\181067). The Natural Environment Research Council (NERC) are thanked for partial funding of the National Environmental Isotope Facility (NEIF; NE/V003917/1). We wish to thank the NERC (NE/V003917/1), the ERC (FP7-IDEAS-ERC/340923) and the University of Bristol for funding GC–MS and GC–IRMS capabilities used for this work. Y.D. and M.G.T. received funding from the ERC Horizon 2020 research and innovation programme (grant agreement no. 788616 YMPACT) and A.T. and M.G.T. received funding from the ERC Horizon 2020 research and innovation programme (grant agreement no. 951385 COREX). G.D.S. and M.S.L. work in the MRC Integrative Epidemiology Unit at the University of Bristol (MC_UU_00011/1). D. Altoft, B. Banecki, L. Benson, P. Bickle (University of York, UK), S. Ferrandin, A. Lafarge, C. Maule (University of Bristol, UK), D. Miernecka, C. Walton-Doyle (University of Manchester, UK) and I. Wiltshire (University of Bristol, UK) are acknowledged for the sampling and/or analysis of some potsherds from this study at the University of Bristol. We thank S. Kalieva and V. Logvin (Kostanay State University, Kazakhstan), C. Lohr (Leibniz Research Institute for Archaeology, Mainz, Germany), J. Lüning (Johann Wolfgang Goethe-Universität, Frankfurt, Germany), I. Pavlů (Institute of Archaeology of the Academy of Sciences of the Czech Republic) and R. W. Schmitz (LVR-LandesMuseum, Bonn, Germany) for providing some of the sherds presented in this study. We are grateful to K. Dwyer, teaching fellow in English grammar and research methodology at University College London (UCL), for clarifying lactase non-persistence as the correct usage over non-lactase persistence, on the basis that ‘non’ qualifies persistence, even if lactase persistence is considered a compound noun. We are also grateful to L. Howe, Senior Research Associate at the MRC IEU for providing derived spousal pairs in UK Biobank. We acknowledge the use of the UCL Computer Science ECON High-Performance Computing (HPC) Cluster (ECON@UCL) and associated support services, in the completion of this work. This study was also supported by the NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.
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R.P.E., M.G.T. and G.D.S. conceived the overall study. M.R.-S. and R.P.E. generated new lipid residue data. M.R.-S., A.T., Y.D. and M.S.L., acquired data, assembled new databases and undertook statistical modelling. G.D.S. and M.S.L. performed the UK Biobank analyses. Y.D., A.T. and M.G.T. conceptualized the selection model likelihood analysis. Y.D. and A.T. performed the selection model testing. A.T. devised the kernel interpolation and generated Figs. 1, 2 and 3. M.G.T., R.P.E., G.D.S., M.R.-S., Y.D., A.T. and M.S.L. wrote the paper. All other authors contributed either critical archaeological information, pottery from excavations, data of various types and expert knowledge. All authors read and approved the manuscript.
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Extended data figures and tables
Extended Data Fig. 1 Regional fluctuations in milk use throughout European prehistory.
Percentage of milk fats through time, calculated using all animal fat residues. Grey bars and black lines illustrate 95%, 50% CI and MAP in each time slice, using a uniform prior.
Extended Data Fig. 2 Summary of model selection results for the tested ecological time series.
Inverse solar insolation, fluctuations in population level, and residential density yield models significantly better than a null model of constant selection (significance computed by likelihood ratio test). See Extended Data Table 1 for corresponding parameter estimates, and multiple testing correction (no change in the set of significant models). Abbreviations: assimilation (assi.), inverse (inv.), fluctuation (fluc.).
Extended Data Fig. 3 Inverse insolation as a driver of selection strength.
Optimized parameters, resulting selection strength- and LP allele frequency curves for inverse (inv.) insolation, one of the four ecological proxy variables yielding likelihoods significantly better than a constant selection model. Although LP is generally thought of as a dominant trait, we only show the additive model results as the parameter estimates barely differ.
Extended Data Fig. 4 Population fluctuation as a driver of selection strength.
Optimized parameters, resulting selection strength- and LP allele frequency curves for population (pop.) fluctuations (fluc.), one of the four ecological proxy variables yielding likelihoods significantly better than a constant selection model. Although LP is generally thought of as a dominant trait, we only show the additive model results as the parameter estimates barely differ.
Extended Data Fig. 5 Settlement density as a driver of selection strength.
Optimized parameters, resulting selection strength- and LP allele frequency curves for the cluster statistic, one of the four ecological proxy variables yielding likelihoods significantly better than a constant selection model. Although LP is generally thought of as a dominant trait, we only show the additive model results as the parameter estimates barely differ.
Extended Data Fig. 6 Wild animal consumption as a driver of selection strength.
Optimized parameters, resulting selection strength- and LP allele frequency curves for proportion of wild versus domestic animal, one of the four ecological proxy variables yielding likelihoods significantly better than a constant selection model. Although LP is generally thought of as a dominant trait, we only show the additive model results as the parameter estimates barely differ.
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Supplementary Table 1 and Figs. 1–9.
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Evershed, R.P., Davey Smith, G., Roffet-Salque, M. et al. Dairying, diseases and the evolution of lactase persistence in Europe. Nature 608, 336–345 (2022). https://doi.org/10.1038/s41586-022-05010-7
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DOI: https://doi.org/10.1038/s41586-022-05010-7
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