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Whole-genome analysis of introgressive hybridization and characterization of the bovine legacy of Mongolian yaks

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Abstract

The yak is remarkable for its adaptation to high altitude and occupies a central place in the economies of the mountainous regions of Asia. At lower elevations, it is common to hybridize yaks with cattle to combine the yak's hardiness with the productivity of cattle. Hybrid males are sterile, however, preventing the establishment of stable hybrid populations, but not a limited introgression after backcrossing several generations of female hybrids to male yaks. Here we inferred bovine haplotypes in the genomes of 76 Mongolian yaks using high-density SNP genotyping and whole-genome sequencing. These yaks inherited ∼1.3% of their genome from bovine ancestors after nearly continuous admixture over at least the last 1,500 years. The introgressed regions are enriched in genes involved in nervous system development and function, and particularly in glutamate metabolism and neurotransmission. We also identified a novel mutation associated with a polled (hornless) phenotype originating from Mongolian Turano cattle. Our results suggest that introgressive hybridization contributed to the improvement of yak management and breeding.

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Figure 1: Phylogenetic analyses of sequence data on chromosomes 9 and 25 confirm bovine introgression in the yak that was sequenced to generate the yak reference genome.
Figure 2: Analysis of the size distribution of introgressed intervals in the yak genome reveals three major introgressions events.
Figure 3: Bovine introgressed segments in yaks show a major enrichment for genes related to nervous system development and function.
Figure 4: Introgression of a novel and complex mutation at the POLLED locus from bovines causes polledness in Mongolian yaks.

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References

  1. Mallet, J. Hybridization as an invasion of the genome. Trends Ecol. Evol. 20, 229–237 (2005).

    PubMed  Google Scholar 

  2. Sankararaman, S. et al. The genomic landscape of Neanderthal ancestry in present-day humans. Nature 507, 354–357 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Vernot, B. & Akey, J.M. Resurrecting surviving Neandertal lineages from modern human genomes. Science 343, 1017–1021 (2014).

    CAS  PubMed  Google Scholar 

  4. Hufford, M.B. et al. The genomic signature of crop–wild introgression in maize. PLoS Genet. 9, e1003477 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Qiu, Q. et al. The yak genome and adaptation to life at high altitude. Nat. Genet. 44, 946–949 (2012).

    CAS  PubMed  Google Scholar 

  6. Qi, X.B., Jianlin, H., Wang, G., Rege, J.E.O. & Hanotte, O. Assessment of cattle genetic introgression into domestic yak populations using mitochondrial and microsatellite DNA markers. Anim. Genet. 41, 242–252 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Zhang, M.Q., Xu, X. & Luo, S.J. The genetics of brown coat color and white spotting in domestic yaks (Bos grunniens). Anim. Genet. 45, 652–659 (2014).

    CAS  PubMed  Google Scholar 

  8. Durkin, K. et al. Serial translocation by means of circular intermediates underlies colour sidedness in cattle. Nature 482, 81–84 (2012).

    CAS  PubMed  Google Scholar 

  9. Medugorac, I. et al. Bovine polledness—an autosomal dominant trait with allelic heterogeneity. PLoS One 7, e39477 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Allais-Bonnet, A. et al. Novel insights into the bovine polled phenotype and horn ontogenesis in Bovidae. PLoS One 8, e63512 (2013).

    PubMed  PubMed Central  Google Scholar 

  11. Rothammer, S. et al. The 80-kb DNA duplication on BTA1 is the only remaining candidate mutation for the polled phenotype of Friesian origin. Genet. Sel. Evol. 46, 44 (2014).

    PubMed  PubMed Central  Google Scholar 

  12. Liu, W.B. et al. Associations of single nucleotide polymorphisms in candidate genes with the polled trait in Datong domestic yaks. Anim. Genet. 45, 138–141 (2014).

    PubMed  Google Scholar 

  13. Daetwyler, H.D. et al. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nat. Genet. 46, 858–865 (2014).

    CAS  PubMed  Google Scholar 

  14. Decker, J.E. et al. Resolving the evolution of extant and extinct ruminants with high-throughput phylogenomics. Proc. Natl. Acad. Sci. USA 106, 18644–18649 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Maples, B.K., Gravel, S., Kenny, E.E. & Bustamante, C.D. RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference. Am. J. Hum. Genet. 93, 278–288 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Wiener, G., Jianlin, H. & Ruijun, L. The Yak (FAO Regional Office for Asia and the Pacific, 2003).

  17. Presgraves, D.C. Sex chromosomes and speciation in Drosophila. Trends Genet. 24, 336–343 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Adhikari, D. & Kumon, F. Climatic changes during the past 1300 years as deduced from the sediments of Lake Nakatsuna, central Japan. Limnology 2, 157–168 (2001).

    CAS  Google Scholar 

  19. Perdue, P.C. China Marches West: The Qing Conquest of Central Eurasia (Harvard University Press, 2009).

  20. Carneiro, M. et al. Rabbit genome analysis reveals a polygenic basis for phenotypic change during domestication. Science 345, 1074–1079 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Schubert, M. et al. Prehistoric genomes reveal the genetic foundation and cost of horse domestication. Proc. Natl. Acad. Sci. USA 111, E5661–E5669 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Secolin, R. et al. Refinement of chromosome 3p22.3 region and identification of a susceptibility gene for bipolar affective disorder. Am. J. Med. Genet. B. Neuropsychiatr. Genet. 162B, 163–168 (2013).

    PubMed  Google Scholar 

  23. Snyder, S.H. & Ferris, C.D. Novel neurotransmitters and their neuropsychiatric relevance. Am. J. Psychiatry 157, 1738–1751 (2000).

    CAS  PubMed  Google Scholar 

  24. Ghasemi, R., Dargahi, L. & Ahmadiani, A. Integrated sphingosine-1 phosphate signaling in the central nervous system: from physiological equilibrium to pathological damage. Pharmacol. Res. 104, 156–164 (2016).

    CAS  PubMed  Google Scholar 

  25. Domschke, K. & Reif, A. Behavioral genetics of affective and anxiety disorders. in Behavioral Neurogenetics (eds. Cryan, F.J. & Reif, A.) 463–502 (Springer, 2012).

  26. Nurnberger, J.I. Jr. et al. Identification of pathways for bipolar disorder: a meta-analysis. JAMA Psychiatry 71, 657–664 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Valdar, W. et al. Genome-wide genetic association of complex traits in heterogeneous stock mice. Nat. Genet. 38, 879–887 (2006).

    CAS  PubMed  Google Scholar 

  28. Johnsson, M., Williams, M.J., Jensen, P. & Wright, D. Genetical genomics of behavior: a novel chicken genomic model for anxiety behavior. Genetics 202, 327–340 (2016).

    CAS  PubMed  Google Scholar 

  29. Zapata, I., Serpell, J.A. & Alvarez, C.E. Genetic mapping of canine fear and aggression. BMC Genomics 17, 572 (2016).

    PubMed  PubMed Central  Google Scholar 

  30. Qiu, Q. et al. Yak whole-genome resequencing reveals domestication signatures and prehistoric population expansions. Nat. Commun. 6, 10283 (2015).

    CAS  PubMed  Google Scholar 

  31. Dove, W.F. The physiology of horn growth: a study of the morphogenesis, the interaction of tissues, and the evolutionary processes of a mendelian recessive character by means of transplantation of tissues. J. Exp. Zool. 69, 347–405 (1935).

    Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  34. Ye, K., Schulz, M.H., Long, Q., Apweiler, R. & Ning, Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25, 2865–2871 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Thorvaldsdóttir, H., Robinson, J.T. & Mesirov, J.P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform. 14, 178–192 (2013).

    Article  PubMed  Google Scholar 

  36. Thompson, J.D., Higgins, D.G. & Gibson, T.J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Tamura, K., Stecher, G., Peterson, D., Filipski, A. & Kumar, S. MEGA6: Molecular Evolutionary Genetics Analysis version 6.0. Mol. Biol. Evol. 30, 2725–2729 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Saitou, N. & Nei, M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425 (1987).

    CAS  PubMed  Google Scholar 

  39. Felsenstein, J. Confidence-limits on phylogenies: an approach using the bootstrap. Evolution 39, 783–791 (1985).

    PubMed  Google Scholar 

  40. Browning, B.L. & Browning, S.R. Efficient multilocus association testing for whole genome association studies using localized haplotype clustering. Genet. Epidemiol. 31, 365–375 (2007).

    PubMed  Google Scholar 

  41. 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 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Leslie, S. et al. The fine-scale genetic structure of the British population. Nature 519, 309–314 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Simčič, M. et al. Recovery of native genetic background in admixed populations using haplotypes, phenotypes, and pedigree information—using Cika cattle as a case breed. PLoS One 10, e0123253 (2015).

    PubMed  PubMed Central  Google Scholar 

  45. Bowcock, A.M. et al. High resolution of human evolutionary trees with polymorphic microsatellites. Nature 368, 455–457 (1994).

    CAS  PubMed  Google Scholar 

  46. Huson, D.H. & Bryant, D. Application of phylogenetic networks in evolutionary studies. Mol. Biol. Evol. 23, 254–267 (2006).

    CAS  PubMed  Google Scholar 

  47. Kuleshov, M.V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44, W90–W97 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Chen, E.Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).

    PubMed  PubMed Central  Google Scholar 

  49. Powell, J.E., Visscher, P.M. & Goddard, M.E. Reconciling the analysis of IBD and IBS in complex trait studies. Nat. Rev. Genet. 11, 800–805 (2010).

    CAS  PubMed  Google Scholar 

  50. Meuwissen, T.H.E. & Goddard, M.E. Prediction of identity by descent probabilities from marker-haplotypes. Genet. Sel. Evol. 33, 605–634 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Lee, S.H. & Van der Werf, J.H.J. Using dominance relationship coefficients based on linkage disequilibrium and linkage with a general complex pedigree to increase mapping resolution. Genetics 174, 1009–1016 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Meuwissen, T.H., Karlsen, A., Lien, S., Olsaker, I. & Goddard, M.E. Fine mapping of a quantitative trait locus for twinning rate using combined linkage and linkage disequilibrium mapping. Genetics 161, 373–379 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Olsen, H.G. et al. Fine mapping of milk production QTL on BTA6 by combined linkage and linkage disequilibrium analysis. J. Dairy Sci. 87, 690–698 (2004).

    CAS  PubMed  Google Scholar 

  54. Kawahara-Miki, R. et al. Whole-genome resequencing shows numerous genes with nonsynonymous SNPs in the Japanese native cattle Kuchinoshima-Ushi. BMC Genomics 12, 103 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, i333–i339 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Corpet, F. Multiple sequence alignment with hierarchical clustering. Nucleic Acids Res. 16, 10881–10890 (1988).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Crooks, G.E., Hon, G., Chandonia, J.M. & Brenner, S.E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

A.C. acknowledges P. Boudinot and D. Boichard for interesting discussions about the major histocompatibility complex and the recombination rate in cattle, respectively; M. Boussaha, Y. Djari and C. Klopp for introducing him to the use of SAMtools and Pindel software; and M.-C. Deloche, C. Escouflaire and A. Michenet for their punctual assistance. The authors acknowledge the Zoologischer Garten Berlin, Tierpark Cottbus and Tierpark Hellabrunn Munich (Germany), as well as the Jardin des Plantes de Paris (France) and all the breeders in Europe and Asia for generously providing samples and phenotypes. Apis-Gène is acknowledged for funding the AKELOS research project. LAFUGA is funded by the LMU excellence program.

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Authors and Affiliations

  1. Chair of Animal Genetics and Husbandry, Ludwig Maximilians University Munich, Munich, Germany

    Ivica Medugorac & Sophie Rothammer

  2. Laboratory for Functional Genome Analysis, Gene Center, Ludwig Maximilians University Munich, Munich, Germany

    Alexander Graf, Helmut Blum & Stefan Krebs

  3. GABI, INRA, AgroParisTech, Université Paris–Saclay, Jouy-en-Josas, France

    Cécile Grohs & Aurélien Capitan

  4. Mongolian Jak Society, Sum, Ulan Bator, Mongolia

    Yondon Zagdsuren

  5. Center of Biotechnology and Molecular Diagnostics of the L.K. Ernst Institute of Animal Husbandry, Moscow region, Russian Federation

    Elena Gladyr & Natalia Zinovieva

  6. INRA, UMR 1388 Génétique, Physiologie et Systèmes d'Elevage GeT-PlaGe Genomic Facility, Castanet-Tolosan, France

    Johanna Barbieri

  7. Université de Toulouse, INPT, ENSAT, UMR 1388 Génétique, Physiologie et Systèmes d'Elevage, Castanet-Tolosan, France

    Johanna Barbieri

  8. Tierzuchtforschung e.V. München, Grub, Germany

    Doris Seichter & Ingolf Russ

  9. AgriGenomics, Illumina, San Diego, California, USA

    André Eggen

  10. Genetics Institute, Faculty of Life Sciences, University College London, London, UK

    Garrett Hellenthal

  11. Department for Biomedical Sciences, Institute of Animal Breeding and Genetics, University of Veterinary Medicine, Vienna, Austria

    Gottfried Brem

  12. ALLICE, Paris, France

    Aurélien Capitan

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  1. Ivica Medugorac
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  2. Alexander Graf
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  3. Cécile Grohs
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  4. Sophie Rothammer
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Contributions

A.C. and I.M. conceived and coordinated the study. A.C., I.M. and S.K. designed the study. I.M. mapped the POLLED locus and performed introgression analysis using SNP chip genotyping data, simulation analyses and neighbor-joining phylogenetic analyses. A.C. performed variant calling, annotation and screening for candidate mutations, analysis of sequence conservation, annotation of the gene content of the introgressed intervals and gene set enrichment analyses. S.K., A.G. and I.M. performed introgression analysis based on WGS data, determination of ancestral alleles, genome and capture sequencing, and R graphics. C.G., S.R. and A.C. performed PCR for Sanger sequencing and for genotyping by PCR and electrophoresis or PCR and Sanger sequencing. J.B. performed WGS. D.S. and I.R. performed SNP chip genotyping and WGS. Y.Z., E.G., N.Z. and G.B. provided samples and phenotypes. H.B. provided sequencing and bioinformatics facilities. A.E. provided Illumina BovineHD SNP chip genotyping data. G.H. provided software and expertise in admixture analysis. A.C., I.M., S.K. and A.G. contributed to writing the manuscript.

Corresponding authors

Correspondence to Ivica Medugorac or Aurélien Capitan.

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Medugorac, I., Graf, A., Grohs, C. et al. Whole-genome analysis of introgressive hybridization and characterization of the bovine legacy of Mongolian yaks. Nat Genet 49, 470–475 (2017). https://doi.org/10.1038/ng.3775

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