Article

Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle

  • Nature Genetics volume 46, pages 858865 (2014)
  • doi:10.1038/ng.3034
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Abstract

The 1000 bull genomes project supports the goal of accelerating the rates of genetic gain in domestic cattle while at the same time considering animal health and welfare by providing the annotated sequence variants and genotypes of key ancestor bulls. In the first phase of the 1000 bull genomes project, we sequenced the whole genomes of 234 cattle to an average of 8.3-fold coverage. This sequencing includes data for 129 individuals from the global Holstein-Friesian population, 43 individuals from the Fleckvieh breed and 15 individuals from the Jersey breed. We identified a total of 28.3 million variants, with an average of 1.44 heterozygous sites per kilobase for each individual. We demonstrate the use of this database in identifying a recessive mutation underlying embryonic death and a dominant mutation underlying lethal chrondrodysplasia. We also performed genome-wide association studies for milk production and curly coat, using imputed sequence variants, and identified variants associated with these traits in cattle.

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References

  1. 1.

    No bull: genes for better milk. Nature 457, 369 (2009).

  2. 2.

    & Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics 185, 623–631 (2010).

  3. 3.

    , & Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions. Heredity 112, 39–47 (2014).

  4. 4.

    et al. Distribution and location of genetic effects for dairy traits. J. Dairy Sci. 92, 2931–2946 (2009).

  5. 5.

    Bovine HapMap Consortium. Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds. Science 324, 528–532 (2009).

  6. 6.

    , & The value of using probabilities of gene origin to measure genetic variability in a population. Genet. Sel. Evol. 29, 5–23 (1997).

  7. 7.

    et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53–59 (2008).

  8. 8.

    et al. A whole-genome assembly of the domestic cow, Bos taurus. Genome Biol. 10, R42 (2009).

  9. 9.

    & Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  10. 10.

    et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

  11. 11.

    & A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am. J. Hum. Genet. 84, 210–223 (2009).

  12. 12.

    et al. Genome-wide association study identifies two major loci affecting calving ease and growth-related traits in cattle. Genetics 187, 289–297 (2011).

  13. 13.

    , , & In-depth annotation of SNPs arising from resequencing projects using NGS-SNP. Bioinformatics 27, 2300–2301 (2011).

  14. 14.

    et al. Whole-genome sequencing and comprehensive variant analysis of a Japanese individual using massively parallel sequencing. Nat. Genet. 42, 931–936 (2010).

  15. 15.

    , & Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4, 1073–1081 (2009).

  16. 16.

    , , & Identification and prevalence of a genetic defect that causes leukocyte adhesion deficiency in Holstein cattle. Proc. Natl. Acad. Sci. USA 89, 9225–9229 (1992).

  17. 17.

    , & The future of livestock breeding: genomic selection for efficiency, reduced emissions intensity, and adaptation. Trends Genet. 29, 206–214 (2013).

  18. 18.

    , , , & Nutritional and flock management options to reduce methane output and methane per unit product from sheep enterprises. Anim. Prod. Sci. 50, 1026–1033 (2010).

  19. 19.

    , , & Harmful recessive effects on fertility detected by absence of homozygous haplotypes. J. Dairy Sci. 94, 6153–6161 (2011).

  20. 20.

    , & SMC2, a Saccharomyces cerevisiae gene essential for chromosome segregation and condensation, defines a subgroup within the SMC family. Genes Dev. 9, 587–599 (1995).

  21. 21.

    , & The condensin complex governs chromosome condensation and mitotic transmission of rDNA. J. Cell Biol. 149, 811–824 (2000).

  22. 22.

    et al. Condensin and Repo-Man–PP1 co-operate in the regulation of chromosome architecture during mitosis. Nat. Cell Biol. 8, 1133–1142 (2006).

  23. 23.

    , & Condensin: architect of mitotic chromosomes. Chromosome Res. 17, 131–144 (2009).

  24. 24.

    , , & Condensin is required for nonhistone protein assembly and structural integrity of vertebrate mitotic chromosomes. Dev. Cell 5, 323–336 (2003).

  25. 25.

    , , , & The Saccharomyces cerevisiae Smc2/4 condensin compacts DNA into (+) chiral structures without net supercoiling. J. Biol. Chem. 280, 34723–34734 (2005).

  26. 26.

    , , , & Mutations in Arabidopsis condensin genes disrupt embryogenesis, meristem organization and segregation of homologous chromosomes during meiosis. Development 130, 3283–3295 (2003).

  27. 27.

    , , & Complex vertebral malformation in Holstein calves. J. Vet. Diagn. Invest. 13, 283–289 (2001).

  28. 28.

    et al. Type II achondrogenesis-hypochondrogenesis: morphologic and immunohistopathologic studies. Am. J. Hum. Genet. 43, 894–903 (1988).

  29. 29.

    et al. Glycine to serine substitution in the triple helical domain of pro-α1 (II) collagen results in a lethal perinatal form of short-limbed dwarfism. J. Biol. Chem. 264, 18265–18267 (1989).

  30. 30.

    et al. A radiographic, morphologic, biochemical and molecular analysis of a case of achondrogenesis type II resulting from substitution for a glycine residue (Gly691→Arg) in the type II collagen trimer. Hum. Mol. Genet. 4, 285–288 (1995).

  31. 31.

    et al. Substitution of aspartic acid for glycine at position 310 in type II collagen produces achondrogenesis II, and substitution of serine at position 805 produces hypochondrogenesis: analysis of genotype-phenotype relationships. Biochem. J. 307, 823–830 (1995).

  32. 32.

    , , , & Widely distributed mutations in the COL2A1 gene produce achondrogenesis type II/hypochondrogenesis. Am. J. Med. Genet. 92, 95–100 (2000).

  33. 33.

    , & Familial chondrodysplasia in Holstein calves. J. Vet. Diagn. Invest. 16, 293–298 (2004).

  34. 34.

    , & A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

  35. 35.

    et al. Genetic and functional confirmation of the causality of the DGAT1 K232A quantitative trait nucleotide in affecting milk yield and composition. Proc. Natl. Acad. Sci. USA 101, 2398–2403 (2004).

  36. 36.

    et al. Mapping of quantitative trait loci controlling tick [Riphicephalus (Boophilus) microplus] resistance on bovine chromosomes 5, 7 and 14. Anim. Genet. 38, 453–459 (2007).

  37. 37.

    et al. Association of BoLA-DRB3.2 alleles with tick (Boophilus microplus) resistance in cattle. Genet. Mol. Res. 5, 513–524 (2006).

  38. 38.

    et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).

  39. 39.

    et al. Mutations in the helix termination motif of mouse type I IRS keratin genes impair the assembly of keratin intermediate filament. Genomics 90, 703–711 (2007).

  40. 40.

    et al. Functional analysis of keratin components in the mouse hair follicle inner root sheath. Br. J. Dermatol. 150, 195–204 (2004).

  41. 41.

    et al. Coat variation in the domestic dog is governed by variants in three genes. Science 326, 150–153 (2009).

  42. 42.

    et al. Morphologic and molecular characterization of two novel Krt71 (Krt2-6g) mutations: Krt71rco12 and Krt71rco13. Mamm. Genome 17, 1172–1182 (2006).

  43. 43.

    et al. Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition. Genome Res. 12, 222–231 (2002).

  44. 44.

    et al. Association of a lysine-232/alanine polymorphism in a bovine gene encoding acyl-CoA:diacylglycerol acyltransferase (DGAT1) with variation at a quantitative trait locus for milk fat content. Proc. Natl. Acad. Sci. USA 99, 9300–9305 (2002).

  45. 45.

    & ACSL1, AGPAT6, FABP3, LPIN1, and SLC27A6 are the most abundant isoforms in bovine mammary tissue and their expression is affected by stage of lactation. J. Nutr. 138, 1019–1024 (2008).

  46. 46.

    et al. Identification and dissection of four major QTL affecting milk fat content in the German Holstein-Friesian population. PLoS ONE 7, e40711 (2012).

  47. 47.

    1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

  48. 48.

    et al. The Drosophila melanogaster Genetic Reference Panel. Nature 482, 173–178 (2012).

  49. 49.

    et al. Bayesian inference of population expansions in domestic bovines. Biol. Lett. 3, 449–452 (2007).

  50. 50.

    , , & A novel predictor of multilocus haplotype homozygosity: comparison with existing predictors. Genet. Res. (Camb.) 91, 413–426 (2009).

  51. 51.

    , , & 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).

  52. 52.

    & New phenotypes for new breeding goals in dairy cattle. Animal 6, 544–550 (2012).

  53. 53.

    et al. Ensembl 2012. Nucleic Acids Res. 40, D84–D90 (2012).

  54. 54.

    NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 41, D8–D20 (2013).

  55. 55.

    UniProt Consortium. Ongoing and future developments at the Universal Protein Resource. Nucleic Acids Res. 39, D214–D219 (2011).

  56. 56.

    & A hidden Markov model combining linkage and linkage disequilibrium information for haplotype reconstruction and quantitative trait locus fine mapping. Genetics 184, 789–798 (2010).

  57. 57.

    , & Multiple sequence alignment using ClustalW and ClustalX. Curr. Protoc. Bioinformatics Chapter 2, Unit 2.3 (2002).

  58. 58.

    & Primer3 on the WWW for general users and for biologist programmers. Methods Mol. Biol. 132, 365–386 (2000).

  59. 59.

    et al. novoSNP, a novel computational tool for sequence variation discovery. Genome Res. 15, 436–442 (2005).

  60. 60.

    , , , & Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).

  61. 61.

    et al. Imputation of high-density genotypes in the Fleckvieh cattle population. Genet. Sel. Evol. 45, 3 (2013).

  62. 62.

    et al. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42, 348–354 (2010).

  63. 63.

    , , , & Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genet. Sel. Evol. 41, 51 (2009).

  64. 64.

    Efficient methods to compute genomic predictions. J. Dairy Sci. 91, 4414–4423 (2008).

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Acknowledgements

H.P. and R.F. acknowledge funding from the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr 'Synbreed—Synergistic Plant and Animal Breeding' (grant 0315527B). We acknowledge the Bavarian State Research Center and Vereinigte Informationssysteme Tierhaltung (VIT) Verden for providing phenotype data. Swissgenetics is acknowledged for providing the DNA material for a Swiss Simmental bull. P.S. and X.L. acknowledge funding from the Genome Canada project entitled 'Whole-Genome Sequence Selection Through Genome-Wide Imputation in Beef Cattle' and acknowledge WestGrid and Compute/Calcul Canada for providing computing resources. D.A.B., A.C., S.F. and D.R. acknowledge the Agence Nationale de la Recherche and Apisgene for funding the Cartoseq project (ANR-10-GENM-0018). T. Simon (EVOLUTION), S. Legrain and P. Laurent (INRA) are acknowledged for providing samples from bulldog calves and information. J.-M. Vacelet (GEN'IAtest), G. Fayolle (UMOTEST) and D. Peinturier (Jura Bétail) are acknowledged for providing samples and phenotypic information for curly coat in Montbeliarde cattle. A. Baur (UNCEIA) and A. Barbat (INRA) are acknowledged for providing haplotype and fertility information for the HH3 study. We would like to thank E. Mullaart (CRV) for providing sequences for ten of the bulls used in this project. B.G., R.F.B. and M.S.L. acknowledge support from grant 3405-10-0137 from the Green Development and Demonstration Programme of the Danish Ministry of Food, Agriculture and Fisheries, the Milk Levy Fund, grant 12-132452 (GenSAP) from the Strategic Research Council and Viking Genetics. C.P.V. acknowledges funding from USDA-ARS project 1245-31000-104-00D and USDA-National Institute of Food and Agriculture (NIFA) competitive grant 2009-65205-05635 from the Animal Genome Program. B.J.H., H.D.D., A.J.C., P.J.B. and M.E.G. acknowledge funding from the Dairy Futures Cooperative Research Centre, Dairy Australia and the Cooperative Research Centre for Beef Genetic Technologies.

Author information

Affiliations

  1. Biosciences Research Division, Department of Environment and Primary Industries, Bundoora, Victoria, Australia.

    • Hans D Daetwyler
    • , Phil J Bowman
    • , David Coote
    • , Amanda J Chamberlain
    • , Charlotte Anderson
    • , Mike E Goddard
    •  & Ben J Hayes
  2. School of Applied Systems Biology, La Trobe University, Bundoora, Victoria, Australia.

    • Hans D Daetwyler
    •  & Ben J Hayes
  3. Dairy Futures Cooperative Research Centre, Bundoora, Victoria, Australia.

    • Hans D Daetwyler
    • , Phil J Bowman
    • , David Coote
    • , Amanda J Chamberlain
    • , Mike E Goddard
    •  & Ben J Hayes
  4. Institut National de la Recherche Agronomique (INRA), UMR 1313 Génétique Animale et Biologie Intégrative, Jouy-en-Josas, France.

    • Aurélien Capitan
    • , Sabrina C Rodriguez
    • , Cécile Grohs
    • , Dominique Rocha
    • , André Eggen
    •  & Didier A Boichard
  5. Union Nationale des Coopératives d'Elevage et d'Insémination Animale, Paris, France.

    • Aurélien Capitan
    •  & Sébastien Fritz
  6. Chair of Animal Breeding, Technische Universitaet Muenchen, Freising-Weihenstephan, Germany.

    • Hubert Pausch
    •  & Ruedi Fries
  7. Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada.

    • Paul Stothard
    •  & Xiaoping Liao
  8. Animal Breeding and Genomics Centre, Wageningen University and Research Centre, Livestock Research, Wageningen, the Netherlands.

    • Rianne van Binsbergen
    • , Ina Hulsegge
    •  & Roel F Veerkamp
  9. Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark.

    • Rasmus F Brøndum
    • , Bernt Guldbrandtsen
    •  & Mogens S Lund
  10. Institut National de la Recherche Agronomique (INRA), Sigenae Bioinformatics Group, UR875, Castanet, France.

    • Anis Djari
    •  & Christophe Klopp
  11. Institut National de la Recherche Agronomique (INRA), Get-Plage, UMR 444 Laboratoire de Génétique Cellulaire, Castanet, France.

    • Diane Esquerré
    •  & Olivier Bouchez
  12. LABOGENA, Jouy-en-Josas, France.

    • Marie-Noëlle Rossignol
  13. US Department of Agriculture, Agricultural Research Service (USDA-ARS), Animal and Natural Resources Institute, Bovine Functional Genomics Laboratory, BARC-East, Beltsville, Maryland, USA.

    • Curt P VanTassell
  14. Faculty of Land and Food Resources, University of Melbourne, Parkville, Victoria, Australia.

    • Mike E Goddard

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Contributions

B.J.H., D.A.B., R.F.V., M.S.L., B.G., M.E.G., R.F., D.R. and C.P.V. designed the study. A.C., H.P., H.D.D., R.v.B., R.F.B. and S.F. performed statistical analyses. P.S. and X.L. performed variant annotation. A.J.C., P.S., X.L., C.A., P.J.B., D.C., I.H. and H.D.D. performed quality control analyses and prepared data. H.D.D., D.C. and P.J.B. designed and developed software to process sequences. A.D., A.E., S.C.R., C.G., D.E., M.-N.R., C.K. and O.B. contributed genotype and phenotype data. H.D.D., R.F., H.P., A.C., D.A.B., R.F.V., B.G., M.S.L., C.P.V., P.S. and B.J.H. contributed to writing the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Ben J Hayes.

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