Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Mapping genes for complex traits in domestic animals and their use in breeding programmes

Key Points

  • Genome-wide association (GWA) studies are being used in livestock, as in humans, to map genes affecting complex traits.

  • SNP panels for use in these GWA studies have recently become commercially available in cattle, dogs, sheep, chickens, pigs and horses.

  • GWA studies have successfully identified mutations causing single-gene traits, such as white spotting in dogs.

  • Associations for complex traits have been reported, but in most cases verification in independent studies has not yet occurred.

  • The SNP panels can be used in the selection of livestock even before they have been used to identify specific mutations causing variation in the economically important traits. This process is called genomic selection. It uses all the SNPs to estimate the genetic value of animals at a young age. By reducing the generation interval, the rate of genetic improvement can be doubled.

  • Genomic selection is already being implemented by dairy industries around the world, and other livestock industries are expected to follow in the near future.

Abstract

Genome-wide panels of SNPs have recently been used in domestic animal species to map and identify genes for many traits and to select genetically desirable livestock. This has led to the discovery of the causal genes and mutations for several single-gene traits but not for complex traits. However, the genetic merit of animals can still be estimated by genomic selection, which uses genome-wide SNP panels as markers and statistical methods that capture the effects of large numbers of SNPs simultaneously. This approach is expected to double the rate of genetic improvement per year in many livestock systems.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Key events in the history of cattle.
Figure 2: Linkage disequilibrium (LD) in cattle breeds.
Figure 3: Calculation of number of animals in a reference population and accuracy of breeding values.

References

  1. 1

    Meuwissen, T. H. E. & Goddard, M. E. The use of marker haplotypes in animal breeding schemes. Genet. Sel. Evol. 28, 161–176 (1996). Quantifies the benefits of MAS.

    Article  PubMed Central  Google Scholar 

  2. 2

    Falconer, D. S. & McKay, T. F. X. Introduction to Quantitative Genetics 4th edn (Longmans Green, UK, 1996).

    Google Scholar 

  3. 3

    Andersson, L. & Georges, M. Domestic animal genomics: deciphering the genetics of complex traits. Nature Rev. Genet. 5, 202–212 (2004).

    CAS  Article  PubMed  Google Scholar 

  4. 3

    Dekkers, J. C. M. & Hospital, F. Multifactorial genetics: the use of molecular genetics in the improvement of agricultural populations. Nature Rev. Genet. 3, 22–32 (2002).

    CAS  Article  PubMed  Google Scholar 

  5. 4

    Van Laere, A. S. et al. A regulatory mutation in IGF2 causes a major QTL effect on muscle growth in the pig. Nature 425, 832–836 (2003).

    CAS  Article  Google Scholar 

  6. 5

    Meuwissen, T. H. E., Hayes, B. J. & Goddard, M. E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829 (2001). Introduced the concept and statistical methods for genomic selection.

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 6

    Tolle, A. in Rep. VIth Int. Bloodgroup Congr. 40–52 (Inst. Blutgruppenforshung, Munich, Germany, 1959).

    Google Scholar 

  8. 7

    Neimann-Sorensen, A. & Robertson, A. The association between blood groups and several production characteristics in three Danish cattle breeds. Acta Agric. Scand. 11, 163–196 (1961).

    Article  Google Scholar 

  9. 8

    Rendel, J. Relationships between blood groups and the fat percentage of the milk in cattle. Nature 189, 408–409 (1961).

    CAS  Article  PubMed  Google Scholar 

  10. 9

    Georges, M. et al. Mapping quantitative trait loci controlling milk production in dairy cattle by exploiting progeny testing. Genetics 139, 907–920 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 10

    Sved, J. A. Linkage disequilibrium and homozygosity of chromosome segments in finite populations. Theor. Popul. Biol. 2, 125–141 (1971).

    CAS  Article  PubMed  Google Scholar 

  12. 11

    Hayes, B. J. Visscher, P. M., McPartlan, H. & Goddard, M. E. A novel multilocus measure of linkage disequilibrium to estimate past effective population size. Genome Res. 13, 635–643 (2003).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 12

    Tenesa, A. et al. Recent human effective population size estimated from linkage disequilibrium. Genome Res. 17, 520–526 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 13

    De Roos, A. P. W., Hayes, B. J., Spelman, R. & Goddard, M. E. Linkage disequilibrium and persistence of phase in Holstein Friesian, Jersey and Angus cattle. Genetics 179, 1503–1512 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 14

    MacEachern, S., Hayes, B. J., McEwan, J. & Goddard, M. E. 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  PubMed  PubMed Central  Google Scholar 

  16. 15

    Sutter, N. B. et al. Extensive and breed-specific linkage disequilibrium in Canis familiaris. Genome Res. 14, 2388–2396 (2004).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. 16

    Meuwissen, T. H. E. & Goddard, M. E. Mapping multiple QTL using linkage disequilibrium and linkage analysis information and multitrait data. Genet. Sel. Evol. 36, 261–279 (2004).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 17

    Uleberg, E. et al. Fine mapping of a QTL for intramuscular fat on porcine chromosome 6 using combined linkage and linkage disequilibrium mapping. J. Anim. Breed Genet. 122, 1–6 (2005).

    CAS  Article  PubMed  Google Scholar 

  19. 18

    Gautier, M. et al. Fine mapping and physical characterization of two linked quantitative trait loci affecting milk fat yield in dairy cattle on BTA26. Genetics 172, 425–436 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 19

    Olsen, H. G., Meuwissen, T. H., Nilsen, H., Svendsen, M & Lien, S. Fine mapping of quantitative trait loci on bovine chromosome 6 affecting calving difficulty. J. Dairy Sci. 91, 4312–4322 (2008).

    CAS  Article  PubMed  Google Scholar 

  21. 20

    Tantia, M. S. et al. DGAT1 and ABCG2 polymorphism in Indian cattle (Bos indicus) and buffalo (Bubalus bubalis) breeds. BMC Vet. Res. 7, 32 (2006).

    Article  Google Scholar 

  22. 21

    Barendse, W., Harrison, B. E., Bunch, R. J. & Thomas, M. B. Variation at the calpain 3 gene is associated with meat tenderness in zebu and composite breeds of cattle. BMC Genet. 9, 41 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  23. 22

    Visscher, P. M. Sizing up human height variation. Nature Genet. 40, 489–490 (2008). Uses published results to demonstrate the small effect size of most QTLs.

    CAS  Article  PubMed  Google Scholar 

  24. 23

    Karlsson, E. K. et al. Efficient mapping of mendelian traits in dogs through genome-wide association. Nature Genet. 39, 1321–1328 (2007). Shows how mapping the same locus within two different breeds of dog can lead to discovery of a causal mutation.

    CAS  Article  Google Scholar 

  25. 24

    Charlier, C. et al. Highly effective SNP-based association mapping and management of recessive defects in livestock. Nature Genet. 40, 449–454 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. 25

    Kolbehdari, D. et al. A whole-genome scan to map quantitative trait loci for conformation and functional traits in Canadian Holstein bulls. J. Dairy Sci. 91, 2844–2856 (2008).

    CAS  Article  PubMed  Google Scholar 

  27. 26

    Daetwyler, H. D., Schenkel, F. S., Sargolzaei, M. & Robinson, J. A. A genome scan to detect quantitative trait loci for economically important traits in Holstein cattle using two methods and a dense single nucleotide polymorphism map. J. Dairy Sci. 91, 3225–3236 (2008).

    CAS  Article  PubMed  Google Scholar 

  28. 27

    Barendse, W. et al. A validated whole-genome association study of efficient food conversion in cattle. Genetics. 176, 1893–1905 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 28

    Lillehammer, M., Hayes, B. J., Meuwissen, T. H. E. & Goddard, M. E. Gene by environment interactions for production traits in Australian dairy cattle. J. Dairy Sci. (in the press).

  30. 29

    Long, N., Gianola, D., Rosa, G. J., Weigel, K. A. & Avendaño, S. Marker-assisted assessment of genotype by environment interaction: a case study of single nucleotide polymorphism-mortality association in broilers in two hygiene environments. J. Anim. Sci. 86, 3358–3366 (2008).

    CAS  Article  PubMed  Google Scholar 

  31. 30

    Hasenstein, J. R., Hassen, A. T., Dekkers, J. C. & Lamont, S. J. High resolution, advanced intercross mapping of host resistance to Salmonella colonization. Dev. Biol. 132, 213–218 (2008).

    CAS  Google Scholar 

  32. 31

    Beavis, W. D. in Molecular Dissection of Complex Traits (ed. Patterson, A. H.) 145–162 (CRC, New York, 1998).

    Google Scholar 

  33. 32

    Sanna, S. et al. Common variants in the GDF5-UQCC region are associated with variation in human height. Nature Genet. 40, 198–203 (2008).

    CAS  Article  Google Scholar 

  34. 33

    Franke, A. et al. Replication of signals from recent studies of Crohn's disease identifies previously unknown disease loci for ulcerative colitis. Nature Genet. 40, 713–715 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. 34

    Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007).

  36. 35

    Jones, P. et al. Single-nucleotide-polymorphism-based association mapping of dog stereotypes. Genetics 179, 1033–1044 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 36

    Spelman, R. J. Ford, C. A., McElhinney, P., Gregory, G. C. & Snell, R. G. Characterization of the DGAT1 gene in the New Zealand dairy population. J. Dairy Sci. 85, 3514–3517 (2002).

    CAS  Article  PubMed  Google Scholar 

  38. 37

    Dunner, S. et al. Haplotype diversity of the myostatin gene among beef cattle breeds. Genet. Sel. Evol. 35, 103–118 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  39. 38

    Smith, C. Improvement of metric traits through specific genetic loci. Anim. Prod. 9, 349–358 (1967).

    Article  Google Scholar 

  40. 39

    Fujii, J. et al. Identification of a mutation in porcine ryanodine receptor associated with malignant hyperthermia. Science 253, 448–451 (1991).

    CAS  Article  PubMed  Google Scholar 

  41. 40

    Piper, L., R., Bindon, B. M. & Davis, G. H. in Genetics of Reproduction in Sheep (eds Land, R. B. & Robinson D. W) 115–125 (Butterworths, London, 1985).

    Book  Google Scholar 

  42. 41

    Shuster, D. E., Kehrli, M. E. Jr, 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 (1982).

    Article  Google Scholar 

  43. 42

    Goldman, W. N. et al. Two alleles of a neural protein gene linked to scrapie in sheep. Proc. Natl Acad. Sci. USA 87, 2476–2480 (1990).

    Article  Google Scholar 

  44. 43

    Davis, G. H. Major genes affecting ovulation rate in sheep. Genet. Sel. Evol. 37 (Suppl. 1), S11–S23 (2005).

    Article  Google Scholar 

  45. 44

    Van Arendonk, J. A. M. et al. in From Jay L. Lush to Genomics: Visions for Animal Breeding and Genetics (eds Dekkers, J. C. M., Lamont, S. J. & Rothschild, M. F.) 60–69 (Iowa State Univ., Ames, 1999).

    Google Scholar 

  46. 45

    Plastow, G. S. et al. in Proceedings 28th Annual Meeting National Swine Improvement Federation 151–154 (Iowa State Univ., Ames, 2003).

    Google Scholar 

  47. 46

    Dekkers, J. C. Commercial application of marker- and gene-assisted selection in livestock: strategies and lessons. J. Anim. Sci. 82, E313–E328 (2004).

    PubMed  Google Scholar 

  48. 47

    Schaeffer, L. R. Strategy for applying genome-wide selection in dairy cattle. J. Anim. Breed. Genet. 123, 218–223 (2006). Calculates the gain in selection response from genomic selection in dairy cattle.

    CAS  Article  PubMed  Google Scholar 

  49. 48

    VanRaden, P. M. et al. Reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci. 92, 16–24 (2009).

    CAS  Article  PubMed  Google Scholar 

  50. 49

    Harris, B. L., Johnson, D. L. & Spelman, R. J. in Proc. Interbull Meeting, Bulletin 39 (Niagara Falls, Canada, 2008).

    Google Scholar 

  51. 50

    Hayes, B. J., Bowman, P. J., Chamberlain, A. C. & Goddard, M. E. Genomic selection in dairy cattle: progress and challenges. J. Dairy Sci. 92, 433–443 (2008).

    Article  Google Scholar 

  52. 51

    Legarra A., Robert-Granié, C., Manfredi, E. & Elsen, J. M. Performance of genomic selection in mice. Genetics. 180, 611–618 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  53. 52

    Lee, S. H., van der Werf, J. H., Hayes, B. J., Goddard, M. E. & Visscher, P. M. Predicting unobserved phenotypes for complex traits from whole-genome SNP data. PLoS Genet. 4, e1000231 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  54. 53

    González-Recio, O., Gianola, D., Rosa, G. J., Weigel, K. A., Kranis, A. Genome-assisted prediction of a quantitative trait measured in parents and progeny: application to food conversion rate in chickens. Genet. Sel. Evol. 41, 3 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  55. 54

    Goddard, M. E. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 14 Aug 2008 (doi: 10.1007/s10709-008-9308-0). Presents formulae for the accuracy of genomic selection and the optimization of long-term selection response.

    Article  PubMed  Google Scholar 

  56. 55

    Goddard, M. E. & Hayes, B. J. Genomic selection. J. Anim. Breed. Genet. 124, 323–330 (2007).

    CAS  Article  Google Scholar 

  57. 56

    Hayes, B. J., Visscher, P. M. & Goddard, M. E. Increased accuracy of selection by using the realised relationship matrix. Genet. Res. 91, 47–60 (2009).

    CAS  Article  Google Scholar 

  58. 57

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

    CAS  Article  PubMed  Google Scholar 

  59. 58

    Maher, B. The case of the missing heritability. Nature 456, 18–21 (2008).

    CAS  Article  PubMed  Google Scholar 

  60. 59

    Willer, C. J. et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nature Genet. 41, 25–34 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  61. 60

    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).

    CAS  Article  PubMed  Google Scholar 

  62. 61

    Hayes, B. J. & Goddard, M. E. The distribution of the effects of genes affecting quantitative traits in livestock. Genet. Sel. Evol. 33, 209–229 (2001).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  63. 62

    Weller, J. I. Shlezinger, M. & Ron, M. Correcting for bias in estimation of quantitative trait loci effects. Genet. Sel. Evol. 37, 501–522 (2005).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  64. 63

    Bellinge, R. H., Liberles, D. A., Iaschi, S. P., O'Brien, P. A. & Tay, G. K. Myostatin and its implications on animal breeding: a review. Anim. Genet. 36, 1–6 (2005).

    CAS  Article  PubMed  Google Scholar 

  65. 64

    Clop, A. et al. A mutation creating a potential illegitimate microRNA target site in the myostatin gene affects muscularity in sheep. Nature Genet. 38, 813–818 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  66. 65

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

  67. 66

    Drögemüller, C. et al. A mutation in hairless dogs implicates FOXI3 in ectodermal development. Science 321, 1462 (2008).

    Article  PubMed  Google Scholar 

  68. 67

    Awano, T. et al. Genome-wide association analysis reveals a SOD1 mutation in canine degenerative myelopathy that resembles amyotrophic lateral sclerosis. Proc. Natl Acad. Sci. USA 106, 2794–2799 (2009).

    CAS  Article  PubMed  Google Scholar 

  69. 68

    Wiik, A. C. et al. A deletion in nephronophthisis 4 (NPHP4) is associated with recessive cone-rod dystrophy in standard wire-haired dachshund. Genome Res. 18, 1415–1421 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  70. 69

    Salmon Hillbertz, N. H. et al. Duplication of FGF3, FGF4, FGF19 and ORAOV1 causes hair ridge and predisposition to dermoid sinus in Ridgeback dogs. Nature Genet. 39, 1318–1320 (2007).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank H. Campbell and H. Burrow for cattle pictures used in this Review.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Michael E. Goddard.

Related links

Related links

FURTHER INFORMATION

APIL's Genomic Comparison of Young Bulls

Glossary

Quantitative trait

A measurable trait that depends on the cumulative action of many genes and the environment, and that can vary among individuals over a given range to produce a continuous distribution of phenotypes.

Estimated breeding value

An estimate of the additive genetic merit for a particular trait that an individual will pass on to its descendents.

Heritability

The proportion of phenotypic variance caused by additive genetic variation.

Genetic improvement

Deliberate genetic change in a population of domestic animals or plants brought about by human control of their selection and breeding that makes them more suitable for the purpose for which they are kept.

Genomic selection

Selection of animals for breeding based on estimated breeding values calculated from the joint effects of genetic markers covering the whole genome.

Linkage disequilibrium

The absence of linkage equilibrium so that the allele at one locus is correlated with the allele at another locus.

Effective population size

The number of individuals in an idealized population with random mating and no selection that would lead to the same rate of inbreeding as observed in the real population. The effective population size can be much less than the actual population size owing to the unequal genetic contribution of individuals to the next generation.

Linear model

A statistical model that assumes that the observed phenotypic value can be explained by the sum of the effects of independent variables and a random error, which is usually assumed to be normally distributed.

Polygenic breeding value

The additive genetic merit an individual passes on to its descendents owing to the combined contribution of many genes of small effect, but possibly excluding some specified genes.

Admixture

A population or sample of individuals derived from more than one race or breed and that have not undergone random mating.

LD phase

If linkage disequilibrium (LD) exists between genes A and B, each with two alleles (A or a and B or b), then gametes that carry allele A can carry B or b. Thus, LD can exist in one of two phases: gametes that are more commonly AB and ab, or gametes that are more commonly Ab and aB.

Beavis effect

The tendency for statistically significant effects to be overestimated when many effects are tested for significance.

Minor allele frequency

The frequency of the less frequent allele in a two-allele polymorphism.

Genomic breeding value

An estimate of an animal's genetic merit, including genomic information

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Goddard, M., Hayes, B. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat Rev Genet 10, 381–391 (2009). https://doi.org/10.1038/nrg2575

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing