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

Nature Genetics volume 46, pages 858–865 (2014)Cite this article

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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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Figure 1: Accuracy of imputing sequence variants.
Figure 2: Sequence-based fine-mapping of a locus underlying curly hair in 2,253 Fleckvieh bulls.
Figure 3: Sequence-based association study for early-lactation milk fat percentage.

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References

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Druet, T., Macleod, I.M. & Hayes, B.J. Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions. Heredity 112, 39–47 (2014).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

  6. Boichard, D., Maignel, L. & Verrier, E. The value of using probabilities of gene origin to measure genetic variability in a population. Genet. Sel. Evol. 29, 5–23 (1997).

    Article  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Browning, B.L. & Browning, S.R. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Grant, J.R., Arantes, A.S., Liao, X. & Stothard, P. In-depth annotation of SNPs arising from resequencing projects using NGS-SNP. Bioinformatics 27, 2300–2301 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  15. Kumar, P., Henikoff, S. & Ng, P.C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4, 1073–1081 (2009).

    Article  CAS  PubMed  Google Scholar 

  16. Shuster, D.E., Kehrli, M.E., Ackermann, M.R. & Gilbert, R.O. Identification and prevalence of a genetic defect that causes leukocyte adhesion deficiency in Holstein cattle. Proc. Natl. Acad. Sci. USA 89, 9225–9229 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Hayes, B.J., Lewin, H.A. & Goddard, M.E. The future of livestock breeding: genomic selection for efficiency, reduced emissions intensity, and adaptation. Trends Genet. 29, 206–214 (2013).

    Article  CAS  PubMed  Google Scholar 

  18. Hegarty, R.S., Alcock, D., Robinson, D.L., Goopy, J.P. & Vercoe, P.E. Nutritional and flock management options to reduce methane output and methane per unit product from sheep enterprises. Anim. Prod. Sci. 50, 1026–1033 (2010).

    Article  CAS  Google Scholar 

  19. VanRaden, P.M., Olson, K.M., Null, D.J. & Hutchison, J.L. Harmful recessive effects on fertility detected by absence of homozygous haplotypes. J. Dairy Sci. 94, 6153–6161 (2011).

    Article  CAS  PubMed  Google Scholar 

  20. Strunnikov, A.V., Hogan, E. & Koshland, D. SMC2, a Saccharomyces cerevisiae gene essential for chromosome segregation and condensation, defines a subgroup within the SMC family. Genes Dev. 9, 587–599 (1995).

    Article  CAS  PubMed  Google Scholar 

  21. Freeman, L., Aragon-Alcaide, L. & Strunnikov, A. The condensin complex governs chromosome condensation and mitotic transmission of rDNA. J. Cell Biol. 149, 811–824 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hudson, D.F., Marshall, K.M. & Earnshaw, W.C. Condensin: architect of mitotic chromosomes. Chromosome Res. 17, 131–144 (2009).

    Article  CAS  PubMed  Google Scholar 

  24. Hudson, D.F., Vagnarelli, P., Gassmann, R. & Earnshaw, W.C. Condensin is required for nonhistone protein assembly and structural integrity of vertebrate mitotic chromosomes. Dev. Cell 5, 323–336 (2003).

    Article  CAS  PubMed  Google Scholar 

  25. Stray, J.E., Crisona, N.J., Belotserkovskii, B.P., Lindsley, J.E. & Cozzarelli, N.R. The Saccharomyces cerevisiae Smc2/4 condensin compacts DNA into (+) chiral structures without net supercoiling. J. Biol. Chem. 280, 34723–34734 (2005).

    Article  CAS  PubMed  Google Scholar 

  26. Siddiqui, N.U., Stronghill, P.E., Dengler, R.E., Hasenkampf, C.A. & Riggs, C.D. Mutations in Arabidopsis condensin genes disrupt embryogenesis, meristem organization and segregation of homologous chromosomes during meiosis. Development 130, 3283–3295 (2003).

    Article  CAS  PubMed  Google Scholar 

  27. Agerholm, J.S., Bendixen, C., Andersen, O. & Arnbjerg, J. Complex vertebral malformation in Holstein calves. J. Vet. Diagn. Invest. 13, 283–289 (2001).

    Article  CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Vissing, H. 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).

    CAS  PubMed  Google Scholar 

  30. Mortier, G.R. 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).

    Article  CAS  PubMed  Google Scholar 

  31. Bonaventure, J. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Körkkö, J., Cohn, D.H., Ala-Kokko, L., Krakow, D. & Prockop, D.J. Widely distributed mutations in the COL2A1 gene produce achondrogenesis type II/hypochondrogenesis. Am. J. Med. Genet. 92, 95–100 (2000).

    Article  PubMed  Google Scholar 

  33. Agerholm, J.S., Arnbjerg, J. & Andersen, O. Familial chondrodysplasia in Holstein calves. J. Vet. Diagn. Invest. 16, 293–298 (2004).

    Article  PubMed  Google Scholar 

  34. Howie, B.N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Grisart, B. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Gasparin, G. 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).

    Article  CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Tanaka, S. 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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  43. Grisart, B. 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).

    Article  CAS  PubMed  Google Scholar 

  44. Winter, A. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Bionaz, M. & Loor, J.J. 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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

  48. Mackay, T.F.C. et al. The Drosophila melanogaster Genetic Reference Panel. Nature 482, 173–178 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. MacLeod, I.M., Meuwissen, T.H.E., Hayes, B.J. & Goddard, M.E. A novel predictor of multilocus haplotype homozygosity: comparison with existing predictors. Genet. Res. (Camb.) 91, 413–426 (2009).

    Article  CAS  Google Scholar 

  51. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

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

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Thompson, J.D., Gibson, T.J. & Higgins, D.G. Multiple sequence alignment using ClustalW and ClustalX. Curr. Protoc. Bioinformatics Chapter 2, Unit 2.3 (2002).

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

    CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G.R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Hayes, B.J., Bowman, P.J., Chamberlain, A.J., Verbyla, K.L. & Goddard, M.E. Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genet. Sel. Evol. 41, 51 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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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

Authors and 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. Department of Molecular Biology and Genetics, Center for Quantitative Genetics and Genomics, 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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  1. Hans D Daetwyler
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  2. Aurélien Capitan
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  6. Rasmus F Brøndum
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  7. Xiaoping Liao
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  9. Sabrina C Rodriguez
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  13. Marie-Noëlle Rossignol
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  18. Phil J Bowman
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  19. David Coote
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  20. Amanda J Chamberlain
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  21. Charlotte Anderson
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  29. Ruedi Fries
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  30. Ben J Hayes
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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.

Corresponding author

Correspondence to Ben J Hayes.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Improvement in accuracy of imputation as a result of BEAGLE correction in Fleckvieh bulls, at increasing fold coverage.

Supplementary Figure 2 The rate of opposing homozygotes from comparison of genotypes for 54 parent-offspring pairs.

Rates for chromosome 1 (a) and chromosome 12 (b). The rate of opposing homozygotes is calculated as the proportion of genotypes within a sliding window of 500 variants that are opposing homozygotes for parent offspring pairs. Genomic regions potentially with problematic assembly are revealed by their high rate of opposing homozygotes, as on chromosome 12. The 1000 bull genomes data has identified a number of such regions.

Supplementary Figure 3 Variants segregating within and across breeds.

Absolute counts of variants segregating within and across breeds (a). Polymorphic sites (SNPs, indels) in randomly selected subsets of 60 animals (15 Angus, 15 Fleckvieh, 15 Holstein, 15 Jersey) were evaluated to assess the number of variants segregating within and across breeds. Across ten replicates, an average number of 8.74 million variants were identified in all 4 breeds (b). Among those, 116,489 variants were homozygous for the alternate allele to the reference sequence in all animals. Holstein and Fleckvieh shared the most variants that were segregating across breeds, most likely reflecting a recent introgression of Holstein into Fleckvieh3. Angus and Jersey shared the least, consistent with FST estimates from array data (The Bovine Hap Consortium, 2009). Compared to Angus, the number of variants segregating within the breed was considerably higher in Fleckvieh, Holstein and Jersey, possibly reflecting that the resequenced animals are key ancestors of these breeds. Fleckvieh had the highest genetic diversity, corroborating its large effective population size4. Principal-component analysis on the sequence variants clearly separated animals by breed (c).

Supplementary Figure 4 Distribution of length of insertion deletions (bp).

(a) All indels. (b) Indels in coding regions.

Supplementary Figure 5 Allele frequency spectrum of variant classes.

(a) Intergenic and intragenic and, for the intragenic variants, synonymous versus nonsynonymous, (b) for nonsynonymous variants, missense and premature stop codons, (c) also for nonsynonymous variants, SIFT predicted tolerated and SIFT predicted deleterious variants. The allele frequency spectrum is reflected around 0.5.

Supplementary Figure 6 Alignment of the SMC2 protein sequences from different eukaryotic organisms around amino acid position F1135 in bovine.

Species names and sequence accession numbers in Ensembl are: Bta, Bos taurus (ENSBTAP00000011562); Has, Homo sapiens (ENSP00000286398); Gga, Gallus gallus (ENSGALP00000036817); Aca, Anolis carolinensis (ENSACAP00000012737); Xtr, Xenopus tropicalis (ENSXETP00000022515); Gmo, Gadus morhua (ENSGMOP00000005092); Csa, Ciona savignyi (ENSCSAVP00000005455); Dme, Drosophila melanogaster (FBpp0086591); Cel, Caenorhabditis elegans (M106.1); Tgo, Toxoplasma gondii (TGME49_097800); Ptr, Phaeodactylum tricornutum (Phatr30352); Ppa, Physcomitrella patens (PP1S52_57V6.1); Ath, Arabidopsis thaliana (AT5G62410.1); Afl, Aspergillus flavus (CADAFLAP00007264); Sce, Saccharomyces cerevisiae (YFR031C). Animals are shown in normal character, protists are underlined, plants are shown in bold type, and fungi are italicized.

Supplementary Figure 7 Identification and characterization of the COL2A1 g.32475732G>A mutation on BTA5, associated with bulldog calf syndrome.

(a) IGV snapshot and (b) DNA sequencing chromatogram showing the g.32475732G>A mutation of a bulldog calf. (b) Genotyping of the same mutation using a PCR-RFLP system (see material and methods). BD, bulldog calves; Mo, wild-type mother of a bulldog calf; IG, IGALE MASC. The 341-bp undigested fragment corresponds to the mutant allele, whereas the 166- and 175-bp fragments result from the digestion of the wild-type allele with BpmI endonuclease. Note incomplete restriction of the IGALE PCR fragment, consistent with mosaicism. (c) DNA sequencing chromatogram showing the g.32475732G>A mutation of a bulldog calf. Confirmation of mosaicism by Sanger sequencing of IGALE PCR fragments prior to RFLP (d) and of a nested PCR on the unrestricted fragment after RFLP (e). (f) Multispecies alignment of the COL2A1 protein sequence around the p.G960R substitution using CLUSTALW. Cattle (Bos taurus), human (Homo sapiens), mouse (Mus musculus), chicken (Gallus gallus), anolis (Anolis carolinensis), Xenopus (Xenopus tropicalis) and zebrafish (Danio rerio) sequence accession numbers are, respectively, ENSBTAP00000017505, ENSP00000369889, ENSMUSP00000023123, ENSGALP00000035064, ENSACAP00000006225, ENSXETP00000043834 and ENSDARP00000047843 in Ensembl . Note the succession of GXY amino acids triplets, typical of the triple-helical domain of collagen proteins, and the perfect across-species conservation of the first G residues.

Supplementary Figure 8 Sequence-based association study for curly hair.

Manhattan plot showing the association of 17,640,970 imputed variants with the proportion of daughters with curly hair in 3222 Fleckvieh bulls (a). Red dots represent variants with P < 10−9. Detailed overview of the associated regions on chromosomes 5 (b) and 19 (c). Variants in the promoter (defined to encompass 1,000 bp upstream of the transcription start), in the untranslated regions (UTR) and in the amino acid coding region are highlighted with different color. The associated region on BTA5 encompasses Krt71, which underlies curly hair in various species. Variant calling yielded four missense mutations in Krt71 (p.R133W, p.F143I, p.N177I, p.P452H); however, none of them was polymorphic in the 43 resequenced Fleckvieh animals. Functional annotation of the variants within the QTL region on BTA5 revealed that 12 closely linked missense mutations in Krt73, Krt2 and Krt76 are highly significantly associated with curly hair in Fleckvieh cattle. Among those, only the p.R201Q mutation in Krt73 (c.G602A, chr. 5: 27,445,800 bp, ss682156288) was predicted to be damaging by PolyPhen-2 and SIFT analysis.

Supplementary Figure 9 Analysis of the sequence conservation of the KRT27 and KRT73 proteins.

(a) Multispecies alignment of the KRT27 proteins from Bos taurus (ENSBTAP00000040718), Procavia capensis (ENSPCAP00000010617), Equus caballus (ENSECAP00000009042), Pteropus vampyrus (ENSPVAP00000010717), Mus musculus (ENSMUSP00000017732), Canis familiaris (ENSCAFP00000023584), Homo sapiens (ENSP00000301656), Loxodonta africana (ENSLAFP00000005744), Erinaceus europaeus (ENSEEUP00000003333), Choloepus hoffmanni (ENSCHOP00000004382), Echinops telfairi (ENSETEP00000010909), Ornithorhynchus anatinus (ENSOANP00000004600), Sarcophilus harrisii (ENSSHAP00000009933) and Monodelphis domestica (ENSMODP00000038545). (b) Multiple-sequence alignment of mammalian-specific paralogs of the KRT27 protein type I keratins within the helix initiation motif in bovine KRT9 (ENSBTAP00000054159), KRT10 (ENSBTAP00000017140), KRT12 (ENSBTAP00000020622), KRT14 (ENSBTAP00000036252), KRT15 (ENSBTAP00000006445), KRT16 (ENSBTAP00000045066), KRT17 (ENSBTAP00000008948), KRT18 (ENSBTAP00000001988), KRT19 (ENSBTAP00000006450), KRT20 (ENSBTAP00000010255), KRT23 (ENSBTAP00000051816), KRT24 (ENSBTAP00000027749), KRT25 (ENSBTAP00000040707), KRT26 (ENSBTAP00000040717), KRT27 (ENSBTAP00000040718), KRT28 (ENSBTAP00000040707), KRT31 (ENSBTAP00000018185), KRT32 (ENSBTAP00000013107), KRT33A (ENSBTAP00000023926), KRT34 (ENSBTAP00000012807), KRT35 (ENSBTAP00000040659), KRT36 (ENSBTAP00000006440), KRT37 (ENSBTAP00000000746), KRT38 (ENSBTAP00000000746) KRT39 (ENSBTAP00000026752) and KRT40 (ENSBTAP00000023156). (c) Multiple-sequence alignment of type I keratins within the helix initiation motif in human. The protein sequence and domain information of human type I keratins was obtained from the Human Intermediate Filament Database (http://www.interfil.org/). KRT24 (ENSBTAP00000027749), KRT25 (ENSBTAP00000040707), KRT26 (ENSBTAP00000040717) and KRT28 (ENSBTAP00000034743). (d) Multispecies alignment of the KRT73 proteins from Bos taurus (ENSBTAP00000010393), Homo sapiens (ENSP00000307014), Pteropus vampyrus (ENSPVAP00000006226), Loxodonta africana (ENSLAFP00000006274), Procavia capensis (ENSPCAP00000005278), Equus caballus (ENSECAP00000012147), Erinaceus europaeus (ENSEEUP00000013894), Mus musculus (ENSMUSP00000065349) and Felis catus (ENSFCAP00000000268).

Supplementary Figure 10 Association of 425,607 SNPs located on BTA19 conditional on the ss699911276 polymorphism.

Black dots represent the P values of the initial screen, and orange dots represent the P values obtained from association analysis conditional on the ss699911276 polymorphism. The red vertical line corresponds to the Bonferroni-corrected threshold of significance (P = 2.8 × 10−9).

Supplementary Figure 11 Sequence-based association study of early-lactation milk fat content on bovine chromosome 14.

The sequence-based association study identified the causal p.A232K polymorphism in Dgat1 among the top association signals in the Holstein breed (a). In the Fleckvieh breed, the association of the causal p.A232K polymorphism in Dgat1 with early-lactation milk fat content is considerably lower compared to the top marker (P = 2.6 × 10−57 versus P = 3.2 × 10−106) (b).

Supplementary Figure 12 Sequence-based association study of early-lactation milk fat content on bovine chromosome 14 in the Fleckvieh breed with an increased reference population.

During the review process of this manuscript, the number of resequenced Fleckvieh animals increased to 78. We reimputed the sequence information for the entire Fleckvieh population on the basis of 78 reference animals and reperformed the association analysis. Now, with an increased reference population, the association of the causal p.A232K polymorphism in Dgat1 with early-lactation milk fat content is only marginally lower compared to the top marker (P = 6.1 × 10−111 versus P = 4.5 × 10−111).

Supplementary Figure 13 Transcription factor binding site (TFBS) prediction for associated promoter variants of AGPAT6 (transcript ID ENSBTAT00000007532).

TFBS were predicted with Alibaba2.1 (red; ref. 5) and Contra (green)6. Only TFBS that differ between the reference and alternative sequence are displayed. The blue box highlights the polymorphic sites.

Supplementary Figure 14 Association studies on chromosome 27 for early-lactation milk fat content with sequence variants imputed from the 1000 bull genomes

In Fleckvieh (a) and Holstein (b) populations. Black dots represent the P values of the initial screen, and orange dots represent the P values after conditioning on the indel polymorphism (36,211,252 bp). The red vertical line represents the Bonferroni-corrected threshold of genome-wide significance (P = 2.8 × 10−9). Conditioning on the indel polymorphism eliminated all association signals in the immediate vicinity of AGPAT6. However, there might be an additional QTL for early-lactation milk fat content segregating in both breeds near the Thrb gene encoding the thyroid hormone receptor beta (green triangle).

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Daetwyler, H., Capitan, A., Pausch, H. et al. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nat Genet 46, 858–865 (2014). https://doi.org/10.1038/ng.3034

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