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The mosaic genome of indigenous African cattle as a unique genetic resource for African pastoralism

Abstract

Cattle pastoralism plays a central role in human livelihood in Africa. However, the genetic history of its success remains unknown. Here, through whole-genome sequence analysis of 172 indigenous African cattle from 16 breeds representative of the main cattle groups, we identify a major taurine × indicine cattle admixture event dated to circa 750–1,050 yr ago, which has shaped the genome of today’s cattle in the Horn of Africa. We identify 16 loci linked to African environmental adaptations across crossbred animals showing an excess of taurine or indicine ancestry. These include immune-, heat-tolerance- and reproduction-related genes. Moreover, we identify one highly divergent locus in African taurine cattle, which is putatively linked to trypanotolerance and present in crossbred cattle living in trypanosomosis-infested areas. Our findings indicate that a combination of past taurine and recent indicine admixture-derived genetic resources is at the root of the present success of African pastoralism.

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Fig. 1: Historical and geographical origin of African cattle breeds in this study.
Fig. 2: Population structure of indigenous African cattle.
Fig. 3: Admixture signatures in African cattle genomes.
Fig. 4: Example of candidate selective loci on BTA7 with an excess of indicine ancestry.
Fig. 5: Example of candidate selective loci on BTA11 with an excess of taurine ancestry.
Fig. 6: Unique selection signatures in African taurine cattle following their separation from the common ancestor with Eurasian taurine cattle.

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Data availability

The newly generated sequences for 114 African cattle and two African buffalo samples are available from the Sequence Read Archive (SRA) with the Bioproject accession number PRJNA574857. The publicly available sequences were downloaded from the SRA and China National GeneBank (CNGB) with the following project accession numbers; CNP0000189 (Achai, Bhagnari, Cholistani, Dajal, Dhanni, Gabrali, Hariana, Lohani, Red Sindhi, Sahiwal and Tharparkar), PRJNA318087 (Angus, Ankole, Jersey, Kenya Boran, Kenana, N’Dama and Ogaden), PRJNA514237 (Boskarin, Limia, Maremmana, Maronesa, Pajuna, Podolica and Sayaguesa), PRJNA324822 (Brahman), PRJNA343262 (Brahman, Gir, Hereford, Nelore and Simmental), PRJNA432125 (Brahman), PRJEB28185 (Eastern Finn and Western Finn), PRJNA210523 (Hanwoo), PRJNA379859 (Hariana, Sahiwal and Thaparkar), PRJNA210521 (Holstein), PRJNA386202 (Muturu) and PRJNA507259 (Nelore). The known variants file (ARS1.2PlusY_BQSR_v3.vcf.gz) for base quality score recalibration was provided by the 1000 Bull Genomes Project (http://www.1000bullgenomes.com/). The annotation of the candidate regions was based on the ARS-UCD1.2 Gene Transfer Format file (.gtf) from Ensembl release 99 (http://www.ensembl.org/). The PANTHER database (http://pantherdb.org/) was used for functional enrichment analysis of a candidate gene set.

References

  1. Schneider, H. K. A model of African indigenous economy and society. Comp. Stud. Soc. Hist. 7, 37–55 (1964).

    Google Scholar 

  2. Di Lernia, S. et al. Inside the ‘African cattle complex’: animal burials in the Holocene central Sahara. PLoS ONE 8, e56879 (2013).

    PubMed  PubMed Central  Google Scholar 

  3. Mwai, O., Hanotte, O., Kwon, Y.-J. & Cho, S. African indigenous cattle: unique genetic resources in a rapidly changing world. Asian-Australas. J. Anim. Sci. 28, 911–921 (2015).

    PubMed  PubMed Central  Google Scholar 

  4. Roberts, C. & Gray, A. Studies on trypanosome-resistant cattle. II. The effect of trypanosomiasis on N’dama, Muturu and Zebu cattle. Trop. Anim. Health Prod. 5, 220–233 (1973).

    CAS  PubMed  Google Scholar 

  5. Hanotte, O. et al. Geographic distribution and frequency of a taurine Bos taurus and an indicine Bos indicus Y specific allele amongst sub‐Saharan African cattle breeds. Mol. Ecol. 9, 387–396 (2000).

    CAS  PubMed  Google Scholar 

  6. Hanotte, O. et al. African pastoralism: genetic imprints of origins and migrations. Science 296, 336–339 (2002).

    CAS  PubMed  Google Scholar 

  7. Loftus, R. T., MacHugh, D. E., Bradley, D. G., Sharp, P. M. & Cunningham, P. Evidence for two independent domestications of cattle. Proc. Natl Acad. Sci. USA 91, 2757–2761 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. MacHugh, D. E., Shriver, M. D., Loftus, R. T., Cunningham, P. & Bradley, D. G. Microsatellite DNA variation and the evolution, domestication and phylogeography of taurine and zebu cattle (Bos taurus and Bos indicus). Genetics 146, 1071–1086 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Achilli, A. et al. Mitochondrial genomes of extinct aurochs survive in domestic cattle. Curr. Biol. 18, R157–R158 (2008).

    CAS  PubMed  Google Scholar 

  10. Bibi, F. A multi-calibrated mitochondrial phylogeny of extant Bovidae (Artiodactyla, Ruminantia) and the importance of the fossil record to systematics. BMC Evolut. Biol. 13, 166 (2013).

    Google Scholar 

  11. Gifford-Gonzalez, D. & Hanotte, O. Domesticating animals in Africa. in The Oxford Handbook of African Archaeology 491–506 (Oxford University Press, 2013).

  12. Blench, R. & MacDonald, K. The Origins and Development of African Livestock: Archaeology, Genetics, Linguistics and Ethnography (Routledge, 2006).

  13. Ajmone‐Marsan, P., Garcia, J. F. & Lenstra, J. A. On the origin of cattle: how aurochs became cattle and colonized the world. Evol. Anthropol. 19, 148–157 (2010).

    Google Scholar 

  14. Manning, K. The first herders of the West African Sahel: inter-site comparative analysis of zooarchaeological data from the lower Tilemsi Valley, Mali. in People and Animals in Holocene Africa. Recent Advances in Archaeozoology 75–85 (Africa Magna, 2011).

  15. Hildebrand, E. A. & Grillo, K. M. Early herders and monumental sites in eastern Africa: dating and interpretation. Antiquity 86, 338–352 (2012).

    Google Scholar 

  16. Chritz, K. L. et al. Climate, ecology, and the spread of herding in eastern Africa. Quat. Sci. Rev. 204, 119–132 (2019).

    Google Scholar 

  17. Lesur, J., Hildebrand, E. A., Abawa, G. & Gutherz, X. The advent of herding in the Horn of Africa: new data from Ethiopia, Djibouti and Somaliland. Quat. Int. 343, 148–158 (2014).

    Google Scholar 

  18. Gifford-Gonzalez, D. & Hanotte, O. Domesticating animals in Africa: implications of genetic and archaeological findings. J. World Prehist. 24, 1–23 (2011).

    Google Scholar 

  19. Epstein, H. The Origin of the Domestic Animals of Africa (Africana Publishing Corporation, 1971).

  20. Gifford-Gonzalez, D. Animal disease challenges to the emergence of pastoralism in sub-Saharan Africa. Afr. Archaeol. Rev. 17, 95–139 (2000).

    Google Scholar 

  21. Sadr, K. The archaeology of herding in southernmost Africa. in The Oxford Handbook of African Archaeology 645–655 (Oxford University Press, 2013).

  22. Gifford-Gonzalez, D. ‘Animal disease challenges’ fifteen years later: the hypothesis in light of new data. Quat. Int. 436, 283–293 (2017).

    Google Scholar 

  23. Felius, M., Koolmees, P. A., Theunissen, B., European Cattle Genetic Diversity Consortium & Lenstra, J. A. On the breeds of cattle—historic and current classifications. Diversity 3, 660–692 (2011).

  24. Freeman, A. et al. Admixture and diversity in West African cattle populations. Mol. Ecol. 13, 3477–3487 (2004).

    CAS  PubMed  Google Scholar 

  25. Bradley, D. G., MacHugh, D. E., Cunningham, P. & Loftus, R. T. Mitochondrial diversity and the origins of African and European cattle. Proc. Natl Acad. Sci. USA 93, 5131–5135 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Bonfiglio, S. et al. Origin and spread of Bos taurus: new clues from mitochondrial genomes belonging to haplogroup T1. PLoS ONE 7, e38601 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Tarekegn, G. M. et al. Variations in mitochondrial cytochrome b region among Ethiopian indigenous cattle populations assert Bos taurus maternal origin and historical dynamics. Asian-Australas. J. Anim. Sci. 31, 1393 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Pérez‐Pardal, L. et al. Y‐specific microsatellites reveal an African subfamily in taurine (Bos taurus) cattle. Anim. Genet. 41, 232–241 (2010).

    PubMed  Google Scholar 

  29. Mbole-Kariuki, M. N. et al. Genome-wide analysis reveals the ancient and recent admixture history of East African Shorthorn Zebu from Western Kenya. Heredity 113, 297 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Bahbahani, H. et al. Signatures of selection for environmental adaptation and zebu × taurine hybrid fitness in East African Shorthorn Zebu. Front. Genet. 8, 68 (2017).

    PubMed  PubMed Central  Google Scholar 

  31. Kim, J. et al. The genome landscape of indigenous African cattle. Genome Biol. 18, 34 (2017).

    PubMed  PubMed Central  Google Scholar 

  32. Verhoeven, K. J., Macel, M., Wolfe, L. M. & Biere, A. Population admixture, biological invasions and the balance between local adaptation and inbreeding depression. Proc. R. Soc. B Biol. Sci. 278, 2–8 (2010).

    Google Scholar 

  33. Hovick, S. M. & Whitney, K. D. Hybridisation is associated with increased fecundity and size in invasive taxa: meta‐analytic support for the hybridisation‐invasion hypothesis. Ecol. Lett. 17, 1464–1477 (2014).

    PubMed  PubMed Central  Google Scholar 

  34. Medugorac, I. et al. Whole-genome analysis of introgressive hybridization and characterization of the bovine legacy of Mongolian yaks. Nat. Genet. 49, 470 (2017).

    CAS  PubMed  Google Scholar 

  35. Chen, N. et al. Whole-genome resequencing reveals world-wide ancestry and adaptive introgression events of domesticated cattle in East Asia. Nat. Commun. 9, 2337 (2018).

    PubMed  PubMed Central  Google Scholar 

  36. Wu, D.-D. et al. Pervasive introgression facilitated domestication and adaptation in the Bos species complex. Nat. Ecol. Evol. 2, 1139–1145 (2018).

    PubMed  Google Scholar 

  37. Wu, C.-I. & Ting, C.-T. Genes and speciation. Nat. Rev. Genet. 5, 114 (2004).

    CAS  PubMed  Google Scholar 

  38. Tijjani, A., Utsunomiya, Y. T., Ezekwe, A., Nash, O. & Hanotte, O. H. Genome sequence analysis reveals selection signatures in endangered trypano-tolerant West African Muturu cattle. Front. Genet. 10, 442 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Bahbahani, H. et al. Signatures of positive selection in African Butana and Kenana dairy zebu cattle. PLoS ONE 13, e0190446 (2018).

    PubMed  PubMed Central  Google Scholar 

  40. Rege, J., Ayalew, W., Getahun, E., Hanotte, O. & Dessie, T. DAGRIS (Domestic Animal Genetic Resources Information System) (International Livestock Research Institute, 2006).

  41. Canavez, F. C. et al. Genome sequence and assembly of Bos indicus. J. Heredity 103, 342–348 (2012).

    CAS  Google Scholar 

  42. Browning, S. R. & Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am. J. Hum. Genet. 81, 1084–1097 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Bahbahani, H., Afana, A. & Wragg, D. Genomic signatures of adaptive introgression and environmental adaptation in the Sheko cattle of southwest Ethiopia. PLoS ONE 13, e0202479 (2018).

    PubMed  PubMed Central  Google Scholar 

  44. Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Patterson, N. et al. Ancient admixture in human history. Genetics 192, 1065–1093 (2012).

    PubMed  PubMed Central  Google Scholar 

  46. Pickrell, J. K. et al. Ancient west Eurasian ancestry in southern and eastern Africa. Proc. Natl Acad. Sci. USA 111, 2632–2637 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Porter, V., Alderson, L., Hall, S. J. & Sponenberg, D. P. Mason’s World Encyclopedia of Livestock Breeds and Breeding (Cabi, 2016).

  48. Rege, J. Zebu Cattle of Kenya: Uses, Performance, Farmer Preferences, Measures of Genetic Diversity and Options for Improved Use (ILRI 2001).

  49. Park, S. D. et al. Genome sequencing of the extinct Eurasian wild aurochs, Bos primigenius, illuminates the phylogeography and evolution of cattle. Genome Biol. 16, 234 (2015).

    PubMed  PubMed Central  Google Scholar 

  50. Hellenthal, G. et al. A genetic atlas of human admixture history. Science 343, 747–751 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Dias-Alves, T., Mairal, J. & Blum, M. G. Loter: a software package to infer local ancestry for a wide range of species. Mol. Biol. Evol. 35, 2318–2326 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Morchikh, M. et al. HEXIM1 and NEAT1 long non-coding RNA form a multi-subunit complex that regulates DNA-mediated innate immune response. Mol. Cell 67, 387–399.e5 (2017).

    CAS  PubMed  Google Scholar 

  53. Flach, H. et al. Mzb1 protein regulates calcium homeostasis, antibody secretion, and integrin activation in innate-like B cells. Immunity 33, 723–735 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Patel, S. & Jin, L. TMEM173 variants and potential importance to human biology and disease. Genes Immun. 20, 82 (2019).

    CAS  PubMed  Google Scholar 

  55. Qiu, X.-B., Shao, Y.-M., Miao, S. & Wang, L. The diversity of the DnaJ/Hsp40 family, the crucial partners for Hsp70 chaperones. Cell. Mol. Life Sci. 63, 2560–2570 (2006).

    CAS  PubMed  Google Scholar 

  56. Delbes, G., Yanagiya, A., Sonenberg, N. & Robaire, B. PABP interacting protein 2 (Paip2) is a major translational regulator involved in the maturation of male germ cells and male fertility. Biol. Reprod. 81, 167–167 (2009).

    Google Scholar 

  57. McReynolds, S. et al. Toward the identification of a subset of unexplained infertility: a sperm proteomic approach. Fertil. Steril. 102, 692–699 (2014).

    PubMed  Google Scholar 

  58. Kuo, Y.-C. et al. SEPT12 orchestrates the formation of mammalian sperm annulus by organizing core octameric complexes with other SEPT proteins. J. Cell Sci. 128, 923–934 (2015).

    CAS  PubMed  Google Scholar 

  59. Zhao, Y. et al. The NLRC4 inflammasome receptors for bacterial flagellin and type III secretion apparatus. Nature 477, 596 (2011).

    CAS  PubMed  Google Scholar 

  60. Canna, S. W. et al. An activating NLRC4 inflammasome mutation causes autoinflammation with recurrent macrophage activation syndrome. Nat. Genet. 46, 1140 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Kitamura, A., Sasaki, Y., Abe, T., Kano, H. & Yasutomo, K. An inherited mutation in NLRC4 causes autoinflammation in human and mice. J. Exp. Med. 211, 2385–2396 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Wang, X. et al. The tick protein Sialostatin L2 binds to Annexin A2 and inhibits NLRC4-mediated inflammasome activation. Infect. Immun. 84, 1796–1805 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Rege, J., Aboagye, G. & Tawah, C. Shorthorn cattle of West and Central Africa. I. Origin, distribution, classification and population statistics. World Anim. Rev. 78, 2–13 (1994).

    Google Scholar 

  64. Yi, X. et al. Sequencing of 50 human exomes reveals adaptation to high altitude. Science 329, 75–78 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. MacEachern, S., Hayes, B., McEwan, J. & Goddard, M. An examination of positive selection and changing effective population size in Angus and Holstein cattle populations (Bos taurus) using a high density SNP genotyping platform and the contribution of ancient polymorphism to genomic diversity in domestic cattle. BMC Genomics 10, 181 (2009).

    PubMed  PubMed Central  Google Scholar 

  66. Flori, L. et al. Adaptive admixture in the West African bovine hybrid zone: insight from the Borgou population. Mol. Ecol. 23, 3241–3257 (2014).

    PubMed  Google Scholar 

  67. Newman, J. L. The Peopling of Africa: A Geographic Interpretation (Yale University Press, 1995).

  68. Russell, J. M., Verschuren, D. & Eggermont, H. Spatial complexity of ‘Little Ice Age’ climate in East Africa: sedimentary records from two crater lake basins in western Uganda. Holocene 17, 183–193 (2007).

    Google Scholar 

  69. Phoofolo, P. Epidemics and revolutions: the rinderpest epidemic in late nineteenth-century Southern Africa. Past Present 138, 112–143 (1993).

    Google Scholar 

  70. Loh, P.-R. et al. Inferring admixture histories of human populations using linkage disequilibrium. Genetics 193, 1233–1254 (2013).

    PubMed  PubMed Central  Google Scholar 

  71. Boivin, N., Crowther, A., Prendergast, M. & Fuller, D. Q. Indian Ocean food globalisation and Africa. Afr. Archaeol. Rev. 31, 547–581 (2014).

    Google Scholar 

  72. Burrow, H. M. et al. Towards a new phenotype for tick resistance in beef and dairy cattle: a review. Anim. Prod. Sci. 59, 1401–1427 (2019).

    CAS  Google Scholar 

  73. Hansen, P. Physiological and cellular adaptations of zebu cattle to thermal stress. Anim. Reprod. Sci. 82, 349–360 (2004).

    PubMed  Google Scholar 

  74. Mirkena, T. et al. Genetics of adaptation in domestic farm animals: a review. Livest. Sci. 132, 1–12 (2010).

    Google Scholar 

  75. Porto-Neto, L. R. et al. Genomic divergence of zebu and taurine cattle identified through high-density SNP genotyping. BMC Genomics 14, 876 (2013).

    PubMed  PubMed Central  Google Scholar 

  76. Bahbahani, H. et al. Signatures of positive selection in East African Shorthorn Zebu: a genome-wide single nucleotide polymorphism analysis. Sci. Rep. 5, 11729 (2015).

    PubMed  PubMed Central  Google Scholar 

  77. Kasarapu, P. et al. The Bos taurusBos indicus balance in fertility and milk related genes. PLoS ONE 12, e0181930 (2017).

    PubMed  PubMed Central  Google Scholar 

  78. Boone, M. & Deen, P. M. Physiology and pathophysiology of the vasopressin-regulated renal water reabsorption. Pflugers Arch. 456, 1005 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Sodhi, M. et al. Microsatellite analysis of genetic population structure of Zebu cattle (Bos indicus) breeds from North-Western region of India. Anim. Biotechnol. 22, 16–29 (2011).

    PubMed  Google Scholar 

  80. Yang, Z. et al. ATG4B (Autophagin-1) phosphorylation modulates autophagy. J. Biol. Chem. 290, 26549–26561 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Ishikawa, H., Ma, Z. & Barber, G. N. STING regulates intracellular DNA-mediated, type I interferon-dependent innate immunity. Nature 461, 788–792 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Yamada, S. et al. Quantitative analysis of cytokine mRNA expression and protozoan DNA load in Theileria parva-infected cattle. J. Vet. Med. Sci. 71, 49–54 (2009).

    CAS  PubMed  Google Scholar 

  83. McElroy, A. K. & Nichol, S. T. Rift Valley fever virus inhibits a pro-inflammatory response in experimentally infected human monocyte derived macrophages and a pro-inflammatory cytokine response may be associated with patient survival during natural infection. Virology 422, 6–12 (2012).

    CAS  PubMed  Google Scholar 

  84. Smetko, A. et al. Trypanosomosis: potential driver of selection in African cattle. Front. Genet. 6, 137 (2015).

    PubMed  PubMed Central  Google Scholar 

  85. Murray, M., Trail, J., Davis, C. & Black, S. Genetic resistance to African trypanosomiasis. J. Infect. Dis. 149, 311–319 (1984).

    CAS  PubMed  Google Scholar 

  86. Safran, M. et al. GeneCards version 3: the human gene integrator. Database 2010, 1–16 (2010).

    Google Scholar 

  87. Pomerantz, J. L., Denny, E. M. & Baltimore, D. CARD11 mediates factor‐specific activation of NF‐κB by the T cell receptor complex. EMBO J. 21, 5184–5194 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Hara, H. et al. The MAGUK family protein CARD11 is essential for lymphocyte activation. Immunity 18, 763–775 (2003).

    CAS  PubMed  Google Scholar 

  89. Noyes, H. et al. Genetic and expression analysis of cattle identifies candidate genes in pathways responding to Trypanosoma congolense infection. Proc. Natl Acad. Sci. USA 108, 9304–9309 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Cecchi, G., Paone, M., Herrero, R. A., Vreysen, M. J. & Mattioli, R. C. Developing a continental atlas of the distribution and trypanosomal infection of tsetse flies (Glossina species). Parasit. Vectors 8, 284 (2015).

    PubMed  PubMed Central  Google Scholar 

  91. Lemecha et al. Response of four indigenous cattle breeds to natural tsetse and trypanosomosis challenge in the Ghibe valley of Ethiopia. Vet. Parasitol. 141, 165–176 (2006).

    CAS  PubMed  Google Scholar 

  92. Naessens, J., Teale, A. & Sileghem, M. Identification of mechanisms of natural resistance to African trypanosomiasis in cattle. Vet. Immunol. Immunopathol. 87, 187–194 (2002).

    CAS  PubMed  Google Scholar 

  93. Hanotte, O. et al. Mapping of quantitative trait loci controlling trypanotolerance in a cross of tolerant West African N’Dama and susceptible East African Boran cattle. Proc. Natl Acad. Sci. USA 100, 7443–7448 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Courtin, D. et al. Host genetics in African trypanosomiasis. Infect. Genet. Evol. 8, 229–238 (2008).

    CAS  PubMed  Google Scholar 

  95. Ciccia, A. et al. Identification of FAAP24, a Fanconi anemia core complex protein that interacts with FANCM. Mol. Cell 25, 331–343 (2007).

    CAS  PubMed  Google Scholar 

  96. Cohn, M. A. et al. A UAF1-containing multisubunit protein complex regulates the Fanconi anemia pathway. Mol. Cell 28, 786–797 (2007).

    CAS  PubMed  Google Scholar 

  97. Kumar, L. et al. Leucine-rich repeat containing 8A (LRRC8A) is essential for T lymphocyte development and function. J. Exp. Med. 211, 929–942 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Ball, E. A. et al. IFNAR1 controls progression to cerebral malaria in children and CD8+ T cell brain pathology in Plasmodium berghei–infected mice. J. Immunol. 190, 5118–5127 (2013).

    CAS  PubMed  Google Scholar 

  99. Makina, S. O. et al. Genome-wide scan for selection signatures in six cattle breeds in South Africa. Genet. Sel. Evol. 47, 92 (2015).

    PubMed  PubMed Central  Google Scholar 

  100. Gautier, M. et al. A whole genome Bayesian scan for adaptive genetic divergence in West African cattle. BMC Genomics 10, 550 (2009).

    PubMed  PubMed Central  Google Scholar 

  101. Kahle, D. & Wickham, H. ggmap: spatial visualization with ggplot2. R. J. 5, 144–161 (2013).

    Google Scholar 

  102. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Lee, H.-J. et al. Deciphering the genetic blueprint behind Holstein milk proteins and production. Genome Biol. Evol. 6, 1366–1374 (2014).

    PubMed  PubMed Central  Google Scholar 

  104. Shin, D.-H. et al. Deleted copy number variation of Hanwoo and Holstein using next generation sequencing at the population level. BMC Genomics 15, 240 (2014).

    PubMed  PubMed Central  Google Scholar 

  105. Heaton, M. P. et al. Using diverse US beef cattle genomes to identify missense mutations in EPAS1, a gene associated with pulmonary hypertension. F1000Res. 5, 2003 (2016).

    PubMed  PubMed Central  Google Scholar 

  106. Taylor, J. F. et al. Lessons for livestock genomics from genome and transcriptome sequencing in cattle and other mammals. Genet. Sel. Evol. 48, 59 (2016).

    PubMed  PubMed Central  Google Scholar 

  107. Andrews, S. FastQC: a quality control tool for high throughput sequence data. Babraham Bioinformatics http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).

  108. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Google Scholar 

  111. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).

    CAS  PubMed  Google Scholar 

  116. Weir, B. S. & Cockerham, C. C. Estimating F‐statistics for the analysis of population structure. Evolution 38, 1358–1370 (1984).

    CAS  PubMed  Google Scholar 

  117. Nguyen, L.-T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).

    CAS  PubMed  Google Scholar 

  118. Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. Felsenstein, J. Evolutionary trees from DNA sequences: a maximum likelihood approach. J. Mol. Evol. 17, 368–376 (1981).

    CAS  PubMed  Google Scholar 

  120. Kousathanas, A. et al. Inferring heterozygosity from ancient and low coverage genomes. Genetics 205, 317–332 (2017).

    PubMed  Google Scholar 

  121. Ma, L. et al. Cattle sex-specific recombination and genetic control from a large pedigree analysis. PLoS Genet. 11, e1005387 (2015).

    PubMed  PubMed Central  Google Scholar 

  122. Lawson, D. J., Hellenthal, G., Myers, S. & Falush, D. Inference of population structure using dense haplotype data. PLoS Genet. 8, e1002453 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  123. Voight, B. F., Kudaravalli, S., Wen, X. & Pritchard, J. K. A map of recent positive selection in the human genome. PLoS Biol. 4, e72 (2006).

    PubMed  PubMed Central  Google Scholar 

  124. Maclean, C. A., Chue Hong, N. P. & Prendergast, J. G. hapbin: an efficient program for performing haplotype-based scans for positive selection in large genomic datasets. Mol. Biol. Evol. 32, 3027–3029 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. Utsunomiya, Y. et al. Genomic clues of the evolutionary history of Bos indicus cattle. Anim. Genet. 50, 557–568 (2019).

    CAS  PubMed  Google Scholar 

  126. Koufariotis, L. et al. Sequencing the mosaic genome of Brahman cattle identifies historic and recent introgression including polled. Sci. Rep. 8, 17761 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. O’brien, A. M. P. et al. Low levels of taurine introgression in the current Brazilian Nelore and Gir indicine cattle populations. Genet. Sel. Evol. 47, 31 (2015).

    PubMed  PubMed Central  Google Scholar 

  128. Zerbino, D. R. et al. Ensembl 2018. Nucleic Acids Res. 46, D754–D761 (2017).

    PubMed Central  Google Scholar 

  129. Mi, H., Muruganujan, A., Ebert, D., Huang, X. & Thomas, P. D. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res. 47, D419–D426 (2019).

    CAS  PubMed  Google Scholar 

  130. Croft, D. et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 42, D472–D477 (2014).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by a grant from the Next-Generation BioGreen 21 Program and Post-Genome Project (Project Nos. PJ01323701 and PJ01040601), Rural Development Administration, Republic of Korea. Sampling of cattle populations was supported by the CGIAR Livestock and Fish CRP (Uganda and Ethiopia), the University of Khartoum (Sudan) and the National Biotechnology Development Agency (NABDA) (Nigeria). The following institutions and their personnel provided help for the sampling of the African cattle: the ILRI Kapiti Ranch; the Ministry of Animal Resources, Fisheries and Range (Sudan); the Ol Pejeta Conservancy (Kenya); the Institute of Biodiversity (Ethiopia); and the Directors of Veterinary Services and the cattle keepers from Ethiopia, Kenya, Uganda and Sudan. The ILRI livestock genomics program is supported by the CGIAR Research Program on Livestock (CRP Livestock), which is supported by contributors to the CGIAR Trust Fund (http://www.cgiar.org/about-us/our-funders/). This research was funded in part by the Bill & Melinda Gates Foundation and with UK aid from the UK Foreign, Commonwealth and Development Office (Grant Agreement OPP1127286) under the auspices of the Centre for Tropical Livestock Genetics and Health (CTLGH), established jointly by the University of Edinburgh, SRUC (Scotland’s Rural College) and the International Livestock Research Institute. The findings and conclusions contained within are those of the authors and do not necessarily reflect positions or policies of the Bill & Melinda Gates Foundation or the UK Government. We thank the reviewers for their critical and constructive comments on the manuscript, and D. Gifford-Gonzalez (University of California, Santa Cruz, CA, USA) for a critical reading of the manuscript in light of the current knowledge on the archeology and history of African pastoralism.

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K.K. and O.H. devised the main conceptual ideas. O.H. and H.K. managed the project. D.L., S.C., S.J.O., H.-K.L., O.A.M., T.D., S.K., O.H. and H.K. conceived of and designed all of the described experiments. O.A.M., T.D., B.S., G.M.T. and A.T. contributed to sample collection and laboratory work. K.K., T.K., D.Y., J. Jang, S.S., S.L., J. Jung and H.J. analyzed the data. K.K., C.J., J.K. and O.H. drafted the manuscript. All authors read and approved the final manuscript.

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Correspondence to Olivier Hanotte or Heebal Kim.

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Extended data

Extended Data Fig. 1 Improvement in genotype concordance after genotype refinement using BEAGLE as a function of depth coverage.

The y-axis shows the concordance between genotypes called from sequencing data compared to genotypes obtained using the BovineSNP50 Genotyping BeadChip.

Extended Data Fig. 2 Delta K of cluster number K in genetic clustering analysis using ADMIXTURE.

A subset of ~1.6 million SNPs (linkage disequilibrium (LD)-based pruning using PLINK v1.9 with ‘-indep-pairwise 50 10 0.1’ option) was used for K from 1 to 10. The delta K analysis suggests K = 2 as the most likely number of distinct genetic ancestries among the 10 Ks (delta K = 31.02).

Extended Data Fig. 3 Mean pairwise Fst values between cattle breeds represented by more than one animal.

Sheko is indicated as yellow.

Extended Data Fig. 4 Estimated heterozygosity of cattle breeds.

The lower and upper bounds of box correspond to the first and third quartiles (the 25th and 75th percentiles), respectively. The horizontal line in the box represents the median value. The upper and lower whisker extend from the bounds to the largest and lowest value no further than 1.5 * interquartile range (IQR), respectively. The number of biologically independent animals used in this analysis for each breed is as follows: Achai (2), Afar (9), Angus (10), Ankole (10), Arsi (10), Barka (9), Bhagnari (3), Boskarin (1), Brahman (20), Butana (20), Cholistani (2), Dajal (1), Dhanni (2), Eastern Finn (5), Ethiopian Boran (10), Fogera (9), Gabrali (2), Gir (4), Goffa (10), Hanwoo (23), Hariana (3), Hereford (18), Holstein (10), Horro (11), Jersey (10), Kenya Boran (10), Kenana (13), Limia (1), Lohani (1), Maremmana (3), Maronesa (1), Mursi (10), Muturu (10), N’Dama (13), Nelore (10), Ogaden (9), Pajuna (2), Poldolica (1), Red Sindhi (1), Sahiwal (2), Sayaguesa (2), Sheko (9), Simmental (11), Tharparkar(3) and Wetern Finn (5). Sheko is indicated as yellow.

Extended Data Fig. 5 Runs of homozygosity patterns of cattle breeds.

Sheko is indicated as yellow.

Extended Data Fig. 6 Weighted LD decay in the Kenya Boran breed before and after fitted with a double-pulse admixture model.

The red curve shows the exponential fit to the data. a, Weighted LD fitted by a single-pulse admixture model, when using EAT and Muturu as a reference population separately. b, Weighted LD fitted by a double-pulse admixture model, when using EAT and Muturu as a reference population separately.

Extended Data Fig. 7 Distribution of proportions of SNPs with |iHS | ≥ 2 and taurine ancestry in each 50-kb window.

a, Distribution of proportions of SNPs with |iHS | ≥ 2. b, Distribution of taurine ancestry. The windows with SNPs less than 10 were removed. Dashed lines indicate the highest 1% for a, and highest or lowest 0.5% in b.

Extended Data Fig. 8 Distribution of taurine ancestry in the candidate regions (the highest 1% for proportion of SNPs with |iHS | ≥ 2), and whole genome windows.

Dashed lines indicate mean (top 1% in iHS analysis: 26.14%, and whole genome: 32.49%).

Extended Data Fig. 9 Distribution of taurine ancestry according to quantiles of proportions of SNPs with |iHS | ≥ 2 in each 50-kb window.

The lower and upper bounds of box correspond to the first and third quartiles (the 25th and 75th percentiles), respectively. The horizontal line in the box represents the median value. The upper and lower whisker extend from the bounds to the largest and lowest value no further than 1.5 * interquartile range (IQR), respectively. Asterisk indicates the highest 1% with proportions of SNPs with |iHS | ≥ 2. n = 149 (African humped cattle) biologically independent animals were used in this analysis.

Extended Data Fig. 10 Distribution of average taurine ancestry generated by resampling random windows (same number of windows as the candidate) for 0.1 million times.

Asterisk indicates average taurine ancestry of the candidate windows from iHS analysis.

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Supplementary Note, Tables 1–13 and Figs. 1–3

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Kim, K., Kwon, T., Dessie, T. et al. The mosaic genome of indigenous African cattle as a unique genetic resource for African pastoralism. Nat Genet 52, 1099–1110 (2020). https://doi.org/10.1038/s41588-020-0694-2

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