Genomic selection has revolutionized dairy cattle breeding. Since 2000, assays have been developed to genotype large numbers of single-nucleotide polymorphisms (SNPs) at relatively low cost. The first commercial SNP genotyping chip was released with a set of 54,001 SNPs in December 2007. Over 15,000 genotypes were used to determine which SNPs should be used in genomic evaluation of US dairy cattle. Official USDA genomic evaluations were first released in January 2009 for Holsteins and Jerseys, in August 2009 for Brown Swiss, in April 2013 for Ayrshires, and in April 2016 for Guernseys. Producers have accepted genomic evaluations as accurate indications of a bull's eventual daughter-based evaluation. The integration of DNA marker technology and genomics into the traditional evaluation system has doubled the rate of genetic progress for traits of economic importance, decreased generation interval, increased selection accuracy, reduced previous costs of progeny testing, and allowed identification of recessive lethals.


Article metrics loading...

Loading full text...

Full text loading...


Literature Cited

  1. Stormont C. 1.  1967. Contribution of blood typing to dairy science progress. J. Dairy Sci. 50:253–60 [Google Scholar]
  2. Spelman RJ. 2.  2002. Utilisation of molecular information in dairy cattle breeding. Proc. World Congr. Genet. Appl. Livest. Prod., 7th, Montpellier, France, Aug. 19–23 Commun. No. 22–02 Castanet-Tolosan, France: INRA [Google Scholar]
  3. Soller M. 3.  1994. Marker assisted selection—an overview. Anim. Biotechnol. 5:193–207 [Google Scholar]
  4. Andersson L. 4.  2001. Genetic dissection of phenotypic diversity in farm animals. Nat. Rev. Genet. 2:130–38 [Google Scholar]
  5. Misztal I. 5.  2006. Challenges of application of marker assisted selection—a review. Anim. Sci. Pap. Rep. 24:5–10 [Google Scholar]
  6. Cole JB, VanRaden PM, O'Connell JR, Van Tassell CP, Sonstegard TS. 6.  et al. 2009. Distribution and location of genetic effects for dairy traits. J. Dairy Sci. 92:2931–46 [Google Scholar]
  7. Meuwissen THE, Hayes BJ, Goddard ME. 7.  2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–29 [Google Scholar]
  8. Bovine HapMap Consort. 8.  2009. Genome-wide survey of SNP variation uncovers the genetic structure of cattle breeds. Science 324:528–32 [Google Scholar]
  9. Khatkar MS, Zenter KR, Hobbs M, Hawken RJ, Cavanagh JAL. 9.  et al. 2007. A primary assembly of a bovine haplotype block map based on a 15,036-single-nucleotide polymorphism panel genotyped in Holstein-Friesian cattle. Genetics 176:763–72 [Google Scholar]
  10. Hayes B, Chamberlain AJ, Goddard ME. 10.  2006. Use of markers in linkage disequilibrium with QTL in breeding programs. Proc. World Congr. Genet. Appl. Livest. Prod., 8th, Belo Horizonte, Brazil, Aug. 13–18 Commun. No. 30–06 Belo Horizonte, Braz.: Inst. Prociência [Google Scholar]
  11. Harhay GP, Smith TPL, Alexander LJ, Haudenschild CD, Keele JW. 11.  et al. 2010. An atlas of bovine gene expression reveals novel distinctive tissue characteristics and evidence for improving genome annotation. Genome Biol 11:R102 [Google Scholar]
  12. Van Tassell CP, Smith TPL, Matukumalli LK, Taylor JF, Schnabel RD. 12.  et al. 2008. SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat. Methods 5:247–52 [Google Scholar]
  13. 13. Bovine Genome Seq. Anal. Consort., Elsik CG, Tellam RL, Worley KC. 2009. The genome sequence of taurine cattle: a window to ruminant biology and evolution. Science 324:522–28 [Google Scholar]
  14. Matukumalli LK, Lawley CT, Schnabel RD, Taylor JF, Allan MF. 14.  et al. 2009. Development and characterization of a high density SNP genotyping assay for cattle. PLOS ONE 4:e5350 [Google Scholar]
  15. 15. Illumina. 2016. BovineSNP50 Genotyping BeadChip Data Sheet, Illumina, San Diego, CA. http://www.illumina.com/Documents/products/datasheets/datasheet_bovine_snp5O.pdf [Google Scholar]
  16. 16. Illumina. 2010. GoldenGate Bovine3K Genotyping BeadChip Data Sheet, Illumina, San Diego, CA. http://www.illumina.com/Documents/products/datasheets/datasheet_bovine3k.pdf [Google Scholar]
  17. 17. Illumina. 2010. BovineHD Genotyping BeadChip Data Sheet, Illumina, San Diego, CA. http://www.illumina.com/Documents/products/datasheets/datasheet_bovineHD.pdf [Google Scholar]
  18. Wiggans GR, Sonstegard TS, VanRaden PM, Matukumalli LK, Schnabel RD. 18.  et al. 2009. Selection of single-nucleotide polymorphisms and quality of genotypes used in genomic evaluation of dairy cattle in the United States and Canada. J. Dairy Sci. 92:3431–36 [Google Scholar]
  19. VanRaden PM. 19.  2008. Efficient methods to compute genomic predictions. J. Dairy Sci. 91:4414–23 [Google Scholar]
  20. VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD. 20.  et al. 2009. Invited review: reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci. 92:16–24 [Google Scholar]
  21. Spelman RJ, Hayes BJ, Berry DP. 21.  2013. Use of molecular technologies for the advancement of animal breeding: genomic selection in dairy cattle populations in Australia, Ireland and New Zealand. Anim. Prod. Sci. 53:869–75 [Google Scholar]
  22. Van Doormaal BJ, Kistemaker GJ, Sullivan PG, Sargolzaei M, Schenkel FS. 22.  2009. Canadian implementation of genomic evaluations. Interbull Bull 40:214–18 [Google Scholar]
  23. David X, de Vries A, Feddersen E, Borchersen S. 23.  2010. International genomic cooperation: EuroGenomics significantly improves reliability of genomic evaluations. Interbull Bull 41:77–78 [Google Scholar]
  24. Druet T, Schrooten C, de Roos APW. 24.  2010. In silico genotyping of thousands of SNPs in dairy cattle for the EuroGenomics project. Proc. World Congr. Genet. Appl. Livest. Prod., 9th, Leipzig, Germany, Aug. 1–6 Commun. No. 0137 Gießen, Ger.: Ges. Tierz. eV [Google Scholar]
  25. 25. Interbull. 2016. Production: April 2016 GMACE Eval., Interbull, Swed. http://www.interbull.org/static/web/g_proddoc1604r.pdf [Google Scholar]
  26. Wiggans GR. 26.  2012. Background on the development of a nonfunded cooperative agreement between USDA's Agricultural Research Service (ARS) and the Council on Dairy Cattle Breeding (CDCB) Anim. Improv. Progr. Lab. Res. Rep. NFCA-CDCB1 (06–12), Agric. Res. Serv., USDA, Beltsville, MD [Google Scholar]
  27. 27. Counc. Dairy Cattle Breed. 2016. Genotypes Included in Evaluations by Breed, Chip Density, Presence of Phenotypes (Old vs. Young), and Evaluation Year-Month (Cumulative) Bowie, MD: Counc. Dairy Cattle Breed https://www.cdcb.us/Genotype/cur_density.html [Google Scholar]
  28. 28. Counc. Dairy Cattle Breed. 2014. Quality Certification Requirements for Genomic Nominators, Effective May 6, 2014 Bowie, MD: Counc. Dairy Cattle Breed https://www.cdcb.us/quality_certification/Quality%20Certification%20Requirements%20for%20Genomic%20Nominators.pdf [Google Scholar]
  29. Wiggans GR, VanRaden PM, Cooper TA. 29.  2011. The genomic evaluation system in the United States: past, present, future. J. Dairy Sci. 94:3202–11 [Google Scholar]
  30. 30. Illumina. 2010. Infinium® Genotyping Data Analysis. Tech. Note, Illumina, San Diego, CA. http://support.illumina.com/content/dam/illumina-marketing/documents/services/technote_infinium_genotyping_data_analysis.pdf [Google Scholar]
  31. Wiggans GR, Cooper TA, VanRaden PM, Van Tassell CP, Bickhart DM, Sonstegard TS. 31.  2016. Increasing the number of single nucleotide polymorphisms used in genomic evaluation of dairy cattle. J. Dairy Sci. 99:4504–11 [Google Scholar]
  32. Johnston J, Kistemaker G, Sullivan PG. 32.  2011. Comparison of different imputation methods. Interbull Bull 44:25–33 [Google Scholar]
  33. VanRaden PM. 33.  2016. findhap.f90: Find Haplotypes and Impute Genotypes using Multiple Chip Sets and Sequence Data. Beltsville, MD: US Dep. Agric http://aipl.arsusda.gov/software/findhap/ [Google Scholar]
  34. Grisart B, Farnir F, Karim L, Cambisano N, Kim J-J. 34.  et al. 2004. Genetic and functional confirmation of the causality of the DGAT1 K232A quantitative trait nucleotide in affecting milk yield and composition. PNAS 101:2398–403 [Google Scholar]
  35. Cole JB, VanRaden PM. 35.  2010. Visualization of results from genomic evaluations. J. Dairy Sci. 93:2727–40 [Google Scholar]
  36. 36. Counc. Dairy Cattle Breed. 2015. SNP Marker Effects for Holstein or Red & White, Calculated April 2015 Bowie, MD: Counc. Dairy Cattle Breed https://www.cdcb.us/Report_Data/Marker_Effects/marker_effects.cfm [Google Scholar]
  37. VanRaden PM. 37.  2004. Invited review: selection on net merit to improve lifetime profit. J. Dairy Sci. 87:3125–31 [Google Scholar]
  38. VanRaden PM, Cole JB. 38.  2014. Net merit as a measure of lifetime profit: 2014 revision Anim. Improv. Progr. Res. Rep. NM5 (10–14), Anim. Genom. Improv. Lab., Agric. Res. Serv., US Dep. Agric., Beltsville, MD. http://aipl.arsusda.gov/reference/nmcalc-2014.htm [Google Scholar]
  39. Wiggans GR, VanRaden PM. 39.  2010. Improved reliability approximation for genomic evaluations in the United States. J. Dairy Sci. 93:E-Suppl. 1533 (Abstr.) [Google Scholar]
  40. Wiggans GR, VanRaden PM, Cooper TA. 40.  2015. Technical note: rapid calculation of genomic evaluations for new animals. J. Dairy Sci. 98:2039–42 [Google Scholar]
  41. García-Ruiz A, Cole JB, VanRaden PM, Wiggans GR, Ruiz-López FJ, Van Tassell CP. 41.  2016. Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. PNAS 113:E3995–4004 [Google Scholar]
  42. VanRaden PM, Cooper TA, Wiggans GR, O'Connell JR, Bacheller LR. 42.  2013. Confirmation and discovery of maternal grandsires and great-grandsires in dairy cattle. J. Dairy Sci. 96:1874–79 [Google Scholar]
  43. VanRaden PM, Sun C, Cooper TA, Null DJ, Cole JB. 43.  2014. Keynote presentation III: Genotypes are useful for more than genomic evaluation. Proc. Int. Comm. Anim. Rec. Sess., 39th, Berlin, Germany, May 19–234 Rome, Italy: ICAR [Google Scholar]
  44. VanRaden PM, Cooper TA. 44.  2015. Genomic evaluations and breed composition for crossbred U.S. dairy cattle. Interbull Bull 49:19–23 [Google Scholar]
  45. 45. Counc. Dairy Cattle Breed. 2016. Trend in Inbreeding Coefficients of Cows for Holstein or Red & White, Calculated April 2016 Bowie, MD: Counc. Dairy Cattle Breed https://www.cdcb.us/eval/summary/inbrd.cfm [Google Scholar]
  46. Sun C, VanRaden PM, O'Connell JR, Weigel KA, Gianola D. 46.  2013. Mating programs including genomic relationships and dominance effects. J. Dairy Sci. 96:8014–23 [Google Scholar]
  47. VanRaden PM, Olson KM, Null DJ, Hutchison JL. 47  2011. Harmful recessive effects on fertility detected by absence of homozygous haplotypes. J. Dairy Sci. 94:6153–61 [Google Scholar]
  48. Cole JB, VanRaden PM, Null DJ, Hutchison JL, Cooper TA, Hubbard SM. 48.  2016. Haplotype tests for recessive disorders that affect fertility and other traits AIP Res. Rep. GENOMIC3 (09–13), Anim. Genom. Improv. Lab., Agric. Res. Serv., US Dep. Agric., Beltsville, MD. http://aipl.arsusda.gov/reference/recessive_haplotypes_ARR-G3.html [Google Scholar]
  49. Gorjanc G, Cleveland MA, Houston RD, Hickey JM. 49.  2015. Potential of genotyping-by-sequencing for genomic selection in livestock populations. Genet. Sel. Evol. 47:12 [Google Scholar]
  50. Fahrenkrug SC, Blake A, Carlson DF, Doran T, Van Eenennaam A. 50.  et al. 2010. Precision genetics for complex objectives in animal agriculture. J. Anim. Sci. 88:2530–39 [Google Scholar]
  51. Tan W, Carlson DF, Walton MW, Fahrenkrug SC, Hackett PB. 51.  2012. Precision editing of large animal genomes. Adv. Genet. 80:37–97 [Google Scholar]
  52. Littlejohn MD, Henty KM, Tiplady K, Johnson T, Harland C. 52.  et al. 2014. Functionally reciprocal mutations of the prolactin signaling pathway define hairy and slick cattle. Nat. Commun. 5:5861 [Google Scholar]
  53. Carlson DF, Lancto CA, Zang B, Kim E-S, Walton M. 53.  et al. 2016. Production of hornless dairy cattle from genome-edited cell lines. Nat. Biotechnol. 34:479–81 [Google Scholar]
  54. Jenko J, Gorjanc G, Mészáros G, Whitelaw CBA, Woolliams JA. 54.  et al. 2014. Use of genome editing in animal breeding programs. Proc. World Congr. Genet. Appl. Livest. Prod., 10th, Vancouver, BC, Canada, Aug. 17–22 Commun. No. 017 Champaign, IL: Am. Soc. Anim. Sci https://asas.org/docs/default-source/wcgalp-proceedings-oral/017_paper_9741_manuscript_912_0.pdf [Google Scholar]

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error