Maternal traces of deep common ancestry and asymmetric gene flow between Pygmy hunter–gatherers and Bantu-speaking farmers Llui´s Quintana-Murcia,b,He´ le` ne Quacha, Christine Harmanta, Francesca Lucaa, Blandine Massonneta, Etienne Patina, Lucas Sicac, Patrick Mouguiama-Daoudad, David Comase, Shay Tzurf, Oleg Balanovskyg, Kenneth K. Kiddh, Judith R. Kiddh, Lolke van der Veend, Jean-Marie Hombertd, Antoine Gessaini, Paul Verduj, Alain Fromentj, Serge Bahuchetj, Evelyne Heyerj, Jean Daussetb,k, Antonio Salasl, and Doron M. Beharf aHuman Evolutionary Genetics Unit, Centre National de la Recherche Scientifique URA3012, Institut Pasteur, 75015 Paris, France; cCentre International de Recherches Me´dicales de Franceville, BP769 Franceville, Gabon; dUnite´Mixte de Recherche 5596, Institut des Sciences de l’Homme, 69363 Lyon, France; eUnitat de Biologia Evolutiva, Departament de Cie`ncies Experimentals i de la Salut, Universitat Pompeu Fabra, 08003 Barcelona, Spain; fMolecular Medicine Laboratory, Rambam Health Care Campus, Haifa 31096, Israel; gRussian Academy of Medical Sciences, 109544 Moscow, Russia; hDepartment of Genetics, Yale University School of Medicine, SHM I-353, New Haven, CT 06510; iUnite´d’Epide´miologie et Physiopathologie des Virus Oncoge`nes, Institut Pasteur, 75015 Paris, France; jMuse´um National d’Histoire Naturelle–Centre National de la Recherche Scientifique-P7 Unite´Mixte de Recherche 5145, Eco-Anthropologie, Muse´e de l’Homme, 75016 Paris, France; kFondation Jean Dausset, Centre d’Etude du Polymorphisme Humain, 75010 Paris, France; and lUnidade de Xene´tica, Instituto de Medicina Legal, Universidad de Santiago de Compostela, 15782 Galicia, Spain Contributed by Jean Dausset, December 6, 2007 (sent for review October 12, 2007) Two groups of populations with completely different life- geographically restricted (5, 6). Modern-day western and eastern styles—the Pygmy hunter–gatherers and the Bantu-speaking Pygmy populations in Central Africa (CA) share distinctive farmers—coexist in Central Africa. We investigated the origins physical and cultural characteristics thought to result from long of these two groups and the interactions between them, by isolation and adaptation to the rainforest (7–9). Two features of analyzing mtDNA variation in 1,404 individuals from 20 farming CA make this a key region for understanding recent human populations and 9 Pygmy populations from Central Africa, with evolution: (i) two groups of populations with completely differ- the aim of shedding light on one of the most fascinating cultural ent lifestyles coexist in this region—Pygmy hunter–gatherer transitions in human evolution (the transition from hunting and (PHG) and Bantu-speaking agricultural (AGR) populations (5, gathering to agriculture). Our data indicate that this region was 6), and (ii) this region is immediately adjacent to the putative site colonized gradually, with an initial L1c-rich ancestral population of origin of Bantu expansions (2–4). ultimately giving rise to current-day farmers, who display var- Studies of contemporary genetic variation in human popula- ious L1c clades, and to Pygmies, in whom L1c1a is the only tions have proved an important tool for investigating human surviving clade. Detailed phylogenetic analysis of complete origins and migratory patterns (10–12). Variations in the ma- mtDNA sequences for L1c1a showed this clade to be autochtho- ternally inherited mtDNA genome have provided evidence nous to Central Africa, with its most recent branches shared supporting both the African origin of modern humans and between farmers and Pygmies. Coalescence analyses revealed subsequent expansion throughout the world (13–20). However, that these two groups arose through a complex evolutionary peopling processes and migration dynamics remain poorly re- process characterized by (i) initial divergence of the ancestors of solved in Africa, especially in CA. Patterns of mtDNA variation contemporary Pygmies from an ancestral Central African popu- in AGR and PHG populations have been investigated in studies lation no more than Ϸ70,000 years ago, (ii) a period of isolation focusing on the control region [usually the hypervariable seg- between the two groups, accounting for their phenotypic dif- ment I (HVS-I)] and a few coding sites (14, 21–35). A number ferences, (iii) long-standing asymmetric maternal gene flow of mtDNA haplogroups (Hgs) have been identified as possible from Pygmies to the ancestors of the farming populations, genetic footprints of Bantu expansions. These Hgs include L0a beginning no more than Ϸ40,000 years ago and persisting until (21, 24, 29), L1c (23, 32), L2a (29, 30), L3b (26), and L3e (27, 28). a few thousand years ago, and (iv) enrichment of the maternal Studies of small numbers of eastern Mbuti PHG, in which only gene pool of the ancestors of the farming populations by the HVS-I was investigated (14), have suggested that the mtDNA arrival and/or subsequent demographic expansion of L0a, L2, gene pool of eastern Pygmies differs substantially from that of and L3 carriers. western PHG, who display genetic similarities to neighboring farming populations (30, 33, 34). However, the actual origins of Africa ͉ evolution ͉ human ͉ mtDNA ͉ populations the PHG and AGR populations and their interactions in space and time remain unclear because of the small samples sizes and odern humans have undergone a major cultural and Mtechnological change: the transition from food collection (hunting–gathering) to food production (agriculture). This tran- Author contributions: L.Q.-M. designed research; H.Q., C.H., F.L., and B.M. performed research; E.P., L.S., P.M.-D., D.C., S.T., O.B., K.K.K., J.R.K., L.v.d.V., J.-M.H., A.G., P.V., A.F., sition has occurred in many parts of the world and began S.B., E.H., and J.D. contributed new reagents/analytic tools; L.Q.-M. and D.M.B. analyzed Ϸ13–10,000 years before the present (YBP). It has made it data; and L.Q.-M., A.S., and D.M.B. wrote the paper. possible for groups to increase in size and to shift from nomad- The authors declare no conflict of interest. ism to sedentarism (1). In subSaharan Africa, agriculture spread Freely available online through the PNAS open access option. much later, expanding from western Central Africa (i.e., eastern Data deposition: The complete mtDNA sequences reported in this paper have been Nigeria and western Cameroon) to much of the East, Central, deposited in the GenBank database (accession nos. EU273476–EU273502). and southern Africa only 3–5,000 YBP. This spread of agricul- bTo whom correspondence may be addressed. E-mail: [email protected] or dausset@ ture was related to the diffusion of Bantu languages (‘‘Bantu cephb.fr. expansions’’) and, possibly, the use of iron (2–4). A few popu- This article contains supporting information online at www.pnas.org/cgi/content/full/ lations, such as the Pygmy hunter–gatherers, did not adopt an 0711467105/DC1. agricultural lifestyle and have remained demographically and © 2008 by The National Academy of Sciences of the USA 1596–1601 ͉ PNAS ͉ February 5, 2008 ͉ vol. 105 ͉ no. 5 www.pnas.org͞cgi͞doi͞10.1073͞pnas.0711467105 Downloaded by guest on September 25, 2021 Table 1. General diversity indices and neutrality tests for the Bantu-speaking AGR and PHG populations studied † ‡ § ¶ ¶ Population (code) Location n* Hg D (SE) Ht D (SE) Pi (SE) Tajima’s D (P) Fu’s FS (P) Agricultural Akele (KEL) Gabon, west 48 0.925 (0.022) 0.985 (0.008) 9.811 (4.571) Ϫ0.70 (0.262) Ϫ16.76 Ateke (TEK) Gabon, southeast 54 0.945 (0.012) 0.985 (0.007) 9.088 (4.248) Ϫ0.76 (0.231) Ϫ21.96 Benga (BEN) Gabon, northwest 50 0.931 (0.016) 0.952 (0.015) 9.922 (4.616) Ϫ0.67 (0.307) Ϫ4.53 (0.101) Duma (DUM) Gabon, east 47 0.925 (0.016) 0.973 (0.010) 9.258 (4.332) Ϫ0.92 (0.193) Ϫ9.09 Eshira (GIS) Gabon, west 40 0.939 (0.016) 0.971 (0.012) 10.077 (4.703) Ϫ0.68 (0.293) Ϫ5.84 (0.060) Eviya (EVI) Gabon, center 38 0.898 (0.023) 0.932 (0.018) 9.135 (4.297) Ϫ0.52 (0.299) Ϫ0.08 (0.539) Ewondo (EWD) Cameroon, west 25 0.900 (0.023) 0.933 (0.023) 9.933 (4.702) 0.05 (0.571) 0.95 (0.692) Fang (FAN-CM) Cameroon, south 39 0.880 (0.028) 0.970 (0.014) 9.333 (4.381) Ϫ0.44 (0.402) Ϫ9.46 Fang (FAN-GB) Gabon, north 66 0.930 (0.012) 0.971 (0.009) 8.849 (4.132) Ϫ0.78 (0.235) Ϫ12.99 Galoa (GAL) Gabon, west 51 0.925 (0.019) 0.965 (0.011) 9.002 (4.214) Ϫ0.96 (0.172) Ϫ6.13 (0.047) Kota (KOT) Gabon, east 56 0.900 (0.023) 0.967 (0.010) 10.562 (4.885) Ϫ0.61 (0.283) Ϫ8.28 (0.021) Makina (MAK) Gabon, center 45 0.928 (0.017) 0.962 (0.016) 9.306 (4.356) Ϫ0.71 (0.269) Ϫ7.28 (0.027) Mitsogo (TSO) Gabon, center 64 0.898 (0.025) 0.961 (0.011) 9.058 (4.224) Ϫ0.84 (0.219) Ϫ9.50 Ndumu (NDU) Gabon, southeast 39 0.953 (0.013) 0.973 (0.013) 9.417 (4.418) Ϫ0.92 (0.178) Ϫ8.01 Ngumba (NGU) Cameroon, west 88 0.932 (0.010) 0.969 (0.007) 10.090 (4.655) Ϫ0.35 (0.435) Ϫ14.10 Nzebi (NZE) Gabon, southeast 63 0.949 (0.010) 0.976 (0.010) 8.955 (4.181) Ϫ1.16 (0.110) Ϫ22.92 Obamba (OBA) Gabon, southeast 47 0.942 (0.016) 0.988 (0.007) 9.741 (4.542) Ϫ1.13 (0.108) Ϫ17.49 Orungu (ORU) Gabon, west 20 0.905 (0.041) 0.974 (0.025) 10.895 (5.173) Ϫ0.13 (0.508) Ϫ3.53 (0.090) Punu (PUN) Gabon, southwest 52 0.946 (0.014) 0.982 (0.007) 9.124 (4.266) Ϫ1.24 (0.096) Ϫ15.94 Shake (SHA) Gabon, east 51 0.899 (0.022) 0.973 (0.011) 10.195 (4.733) Ϫ0.68 (0.275) Ϫ13.01 Eastern Pygmy Mbuti (MBU) DRC 39 0.710 (0.041) 0.823 (0.034) 6.877 (3.307) 1.05 (0.886) 2.69 (0.851) Western Pygmy Babongo (BAB) Gabon, southeast 45 0.721 (0.052) 0.749 (0.058) 6.945 (3.327) Ϫ0.27 (0.493) 1.75 (0.799) Baka (BAK-CC) Cameroon, center 30 0.540 (0.080) 0.830 (0.035) 5.425 (2.688) 0.26 (0.655) 3.34 (0.899) Baka (BAK-CW) Cameroon, southwest 58 0.654 (0.040) 0.786 (0.037) 5.667 (2.757) Ϫ0.75 (0.256) 1.52 (0.766) Baka (BAK-GB) Gabon, northeast 39 0.533 (0.034) 0.757 (0.052) 4.124 (2.098) 0.08 (0.569) 2.76 (0.886) Bakola (BAKO) Cameroon, west 88 0.455 (0.033) 0.722 (0.024) 3.509 (1.805) 2.01 (0.971) 5.05 (0.942) Bakoya (BKY) Gabon, northeast 31 0.333 (0.096) 0.548 (0.087) 3.011 (1.614) Ϫ0.99 (0.190) 4.26 (0.954) Biaka (BIA) CAR 56 0.724 (0.030) 0.823 (0.030) 6.006 (2.906) 0.05 (0.632) 2.42 (0.857) Tikar (BEZ) Cameroon, north 35 0.464 (0.054) 0.703 (0.027) 2.911 (1.565) 1.46 (0.923) 4.38 (0.955) *Sample size.