Sustainable Food Production

2013 Edition
| Editors: Paul Christou, Roxana Savin, Barry A. Costa-Pierce, Ignacy Misztal, C. Bruce A. Whitelaw

Animal Breeding, Foundations of

  • Guilherme J. M. RosaEmail author
Reference work entry

Definition of the Subject

The term Animal Breeding refers to the human-guided genetic improvement of phenotypic traits in domestic animals such as livestock and companion species [1]. Animal breeding is based on principles of Quantitative Genetics [2, 3, 4] and aims to increase the frequency of favorable alleles and allelic combinations in the population, which is achieved through selection of superior individuals and specific mating systems strategies. Selection methods and mating strategies are developed by combining principles of quantitative and population genetics with sophisticated statistical methods and computational algorithms for integrating phenotypic, pedigree, and genomic information, along with the utilization of reproductive technologies that allow for larger progeny cohorts from superior animals as well as shorter generation intervals.

Through selection and mating of superior animals the frequency of favorable alleles is increased, so the overall additive...

This is a preview of subscription content, log in to check access.


Primary Literature

  1. 1.
    Lush JL (1994) The genetics of populations. (Prepared for publication by Chapman AB, Shrode RR, with an addendum by Crow JF) Special report 94, College of Agriculture, Iowa State University, AmesGoogle Scholar
  2. 2.
    Bulmer MG (1985) The mathematical theory of quantitative genetics. Clarendon, OxfordGoogle Scholar
  3. 3.
    Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Longmans Green, HarlowGoogle Scholar
  4. 4.
    Lynch M, Walsh B (1998) Genetic analysis of quantitative traits. Sinauer Associates, SunderlandGoogle Scholar
  5. 5.
    Hill WG (1969) On the theory of artificial selection in finite populations. Genet Res 13:143–163PubMedCrossRefGoogle Scholar
  6. 6.
    Havenstein B, Ferket PR, Qureshi MA (2003) Growth, livability, and feed conversion of 1957 versus 2001 broilers when fed representative 1957 and 2001 broiler diets. Poult Sci 82:1509–1518PubMedGoogle Scholar
  7. 7.
    Bourdon RM (2000) Understanding animal breeding, 2nd edn. Prentice Hall, Upper Saddle RiverGoogle Scholar
  8. 8.
    Crow J, Kimura M (1970) An introduction to populations genetics theory. Haraper and Row, New YorkGoogle Scholar
  9. 9.
    Shook GE (2006) Major advances in determining appropriate selection goals. J Dairy Sci 89(4):1349–1361PubMedCrossRefGoogle Scholar
  10. 10.
    Henderson CR (1949) Estimation of changes in herd environment. J Dairy Sci 32:709Google Scholar
  11. 11.
    Henderson CR (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics 31:423–447PubMedCrossRefGoogle Scholar
  12. 12.
    Henderson CR (1984) Applications of linear models in animal breeding. University of Guelph, GuelphGoogle Scholar
  13. 13.
    Fernando RL, Grossman M (1989) Marker-assisted selection using best linear unbiased prediction. Genet Sel Evol 21:467–477CrossRefGoogle Scholar
  14. 14.
    Yu J et al (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208PubMedCrossRefGoogle Scholar
  15. 15.
    Wolfinger RD, Gibson G, Wolfinger ED, Bennett L, Hamadeh H, Bushel P, Afshari C, Paules RS (2001) Assessing gene significance from cDNA microarray expression data via mixed models. J Comput Biol 8:625–637PubMedCrossRefGoogle Scholar
  16. 16.
    Rosa GJM, Steibel JP, Tempelman RJ (2005) Reassessing design and analysis of two-color microarray experiments using mixed effects models. Comp Funct Genomics 6:123–131PubMedCrossRefGoogle Scholar
  17. 17.
    Steibel JP, Poletto R, Coussens PM, Rosa GJM (2009) A powerful and flexible linear mixed model framework for the analysis of relative quantification RT-PCR data. Genomics 94:146–152PubMedCrossRefGoogle Scholar
  18. 18.
    Henderson CR (1950) Estimation of genetic parameters. Ann Math Stat 21:309Google Scholar
  19. 19.
    Henderson CR (1953) Estimation of variance and covariance components. Biometrics 9:226CrossRefGoogle Scholar
  20. 20.
    Rao CR (1971) Estimation of variance and covariance components MINQUE theory. J Multivar Anal 1:257–275CrossRefGoogle Scholar
  21. 21.
    Harville DA (1977) Maximum likelihood approaches to variance component estimation and to related problems. J Am Stat Assoc 72(358):320–338CrossRefGoogle Scholar
  22. 22.
    Patterson HD, Thompson R (1971) Recovery of inter-block information when block sizes are unequal. Biometrika 58(3):545–554CrossRefGoogle Scholar
  23. 23.
    Sorensen D, Gianola D (2002) Likelihood, Bayesian, and MCMC methods in quantitative genetics. Springer, New YorkGoogle Scholar
  24. 24.
    Littell RC, Miliken GA, Stroup WW, Wolfinger RD (2006) SAS system for mixed models, 2nd edn. SAS Institute, CaryGoogle Scholar
  25. 25.
    Pinheiro JC, Bates DM (2000) Mixed-effects models in S and S-Plus. Springer, New YorkCrossRefGoogle Scholar
  26. 26.
    Searle SR, Casella G, McCulloch CE (1992) Variance components. Wiley, New YorkCrossRefGoogle Scholar
  27. 27.
    Verbeke G, Molenberghs G (1997) Linear mixed models in practice: A SAS-oriented approach, Lecture Notes in Statistics 126. Springer, New YorkCrossRefGoogle Scholar
  28. 28.
    Wright S (1921) Systems of mating I. The biometric relations between parents and offspring. Genetics 6:111–123PubMedGoogle Scholar
  29. 29.
    Henderson CR (1976) A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics 32:69–83CrossRefGoogle Scholar
  30. 30.
    Quaas RL (1976) Computing the diagonal elements of a large numerator relationship matrix. Biometrics 32:949–953CrossRefGoogle Scholar
  31. 31.
    Henderson CR, Quaas RL (1976) Multiple trait evaluation using relatives’ records. J Anim Sci 43:1188–1197Google Scholar
  32. 32.
    Schaeffer LR (1984) Sire and cow evaluation under multiple trait models. J Dairy Sci 67:1567–1580CrossRefGoogle Scholar
  33. 33.
    Thompson R (1977) Estimation of quantitative genetic parameters. In: Pollak E, Kempthorne O, Bailey TB (eds) Proceedings of the international conference on quantitative genetics, Iowa State University Press, Ames, pp. 639–657Google Scholar
  34. 34.
    Meyer K (1985) Maximum-likelihood estimation of variance-components for a multivariate mixed model with equal design matrices. Biometrics 41:153PubMedCrossRefGoogle Scholar
  35. 35.
    Ducrocq V, Besbes B (1993) Solution of multiple trait animal models with missing data on some traits. J Anim Breed Genet 110:81–92PubMedCrossRefGoogle Scholar
  36. 36.
    Quaas RL, Pollak EJ (1981) Modified equations for sire models with groups. J Dairy Sci 64:1868–1872CrossRefGoogle Scholar
  37. 37.
    Quaas RL, Pollak EJ (1980) Mixed model methodology for farm and ranch beef cattle testing programs. J Anim Sci 51:1277–1287Google Scholar
  38. 38.
    Misztal I, Gianola D (1988) Indirect solution of mixed model equations. J Dairy Sci 77(Suppl 2):99–106CrossRefGoogle Scholar
  39. 39.
    Schaeffer LR, Kennedy BW (1986) Computing solutions to mixed model equations. In: 3rd World congress on genetic applied livestock production, Lincoln, Nebraska, 16–22 July 1986, vol XII. pp 382–393Google Scholar
  40. 40.
    Lander ES, Botstein D (1989) Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185–199PubMedGoogle Scholar
  41. 41.
    Haley CS, Knott SA (1992) A simple regression method to for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315–324PubMedCrossRefGoogle Scholar
  42. 42.
    Haley CS, Knott SA, Elsen J-M (1994) Mapping quantitative trait loci in crosses between outbred lines using least squares. Genetics 136:1195–1207PubMedGoogle Scholar
  43. 43.
    Pérez-Enciso M, Misztal I (2004) Qxpak: a versatile mixed model application for genetical genomics and QTL analyses. Bioinformatics 20(16):2792–2798PubMedCrossRefGoogle Scholar
  44. 44.
    Meuwissen THE, Goddard ME (2000) Fine mapping of quantitative trait loci using linkage disequilibria with closely linked marker loci. Genetics 155:421–430PubMedGoogle Scholar
  45. 45.
    Pérez-Enciso M (2003) Fine mapping of complex trait genes combining pedigree and linkage disequilibrium information: a Bayesian unified framework. Genetics 163:1497–1510PubMedGoogle Scholar
  46. 46.
    Lande R, Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:743–756PubMedGoogle Scholar
  47. 47.
    Dekkers JCM, Hospital F (2002) The use of molecular genetics in the improvement of agricultural populations. Nat Rev Genet 3(1):22–32PubMedCrossRefGoogle Scholar
  48. 48.
    Dekkers JCM, van Arendonk JAM (1998) Optimizing selection for quantitative traits with information on an identified locus in outbred populations. Genet Res 71(3):257–275CrossRefGoogle Scholar
  49. 49.
    Manfredi E, Barbieri M, Fournet F, Elsen JM (1998) A dynamic deterministic model to evaluate breeding strategies under mixed inheritance. Genet Sel Evol 30:127–148CrossRefGoogle Scholar
  50. 50.
    Chakraborty R, Moreau L, Dekkers JCM (2002) A method to optimize selection on multiple identified quantitative trait loci. Genet Sel Evol 34(2):145–170PubMedCrossRefGoogle Scholar
  51. 51.
    Goddard ME (1992) A mixed model for analyses of data on multiple genetic-markers. Theor Appl Genet 83:878–886CrossRefGoogle Scholar
  52. 52.
    Goddard ME, Hayes BJ (2007) Genomic selection. J Anim Breed Gen 124(6):323–330CrossRefGoogle Scholar
  53. 53.
    Schaeffer LR (2006) Strategy for applying genome-wide selection in dairy cattle. J Anim Breed Genet 123:218–223PubMedCrossRefGoogle Scholar
  54. 54.
    Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedGoogle Scholar
  55. 55.
    Whittaker JC, Thompson R, Visscher PM (2000) Marker-assisted selection using ridge regression. Genet Res 75:249–252PubMedCrossRefGoogle Scholar
  56. 56.
    Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc B 58:267–288Google Scholar
  57. 57.
    Gianola D, Perez-Enciso M, Toro MA (2003) On marker-assisted prediction of genetic value: beyond the ridge. Genetics 163:347–365PubMedGoogle Scholar
  58. 58.
    Xu S (2003) Estimating polygenic effects using markers of the entire genome. Genetics 163(2):789–801PubMedGoogle Scholar
  59. 59.
    ter Braak CJF, Boer MP, Bink MCAM (2005) Extending Xu’s Bayesian model for estimating polygenic effects using markers of the entire genome. Genetics 170(3):1435–1438PubMedCrossRefGoogle Scholar
  60. 60.
    Hastie T, Tibshirani R, Friedman JH (2001) The elements of statistical learning: data mining, inference, and predictions. Springer, New YorkGoogle Scholar
  61. 61.
    Calus MPL, Veerkamp RF (2007) Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM. J Anim Breed Genet 124:362–368PubMedCrossRefGoogle Scholar
  62. 62.
    Muir WM (2007) Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. J Anim Breed Genet 124:342–355PubMedCrossRefGoogle Scholar
  63. 63.
    VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor J, Schenkel FS (2009) Reliability of genomic predictions for North American dairy bulls. J Dairy Sci 92:16–24PubMedCrossRefGoogle Scholar
  64. 64.
    Weigel KA, de Los Campos G, González-Recio O, Naya H, Wu XL, Long N, Long N, Rosa GJM, Gianola D (2009) Predictive ability of direct genomic values for lifetime net merit of Holstein sires using selected subsets of single nucleotide polymorphism markers. J Dairy Sci 92:5248–5257PubMedCrossRefGoogle Scholar
  65. 65.
    Henderson CR (1985) Best linear unbiased prediction of non-additive genetic merits in non-inbred populations. J Anim Sci 60:111–117Google Scholar
  66. 66.
    Hoeschele I, VanRaden PM (1991) Rapid inverse of dominance relationship matrices for noninbred populations by including sire and dam subclass effects. J Dairy Sci 74:557–569PubMedCrossRefGoogle Scholar
  67. 67.
    Gianola D (1982) Theory and analysis of threshold characters. J Anim Sci 54:1079–1096Google Scholar
  68. 68.
    Gianola D, Foulley JL (1983) Sire evaluation for ordered categorical-data with a threshold-model. Genet Sel Evol 15(2):201–223PubMedCrossRefGoogle Scholar
  69. 69.
    Tempelman RJ, Gianola D (1996) A mixed effects model for overdispersed count data in animal breeding. Biometrics 52:265–279CrossRefGoogle Scholar
  70. 70.
    Strandén I, Gianola D (1998) Attenuating effects of preferential treatment with student-t mixed linear models: a simulation study. Genet Sel Evol 31:25–42CrossRefGoogle Scholar
  71. 71.
    Rosa GJM, Padovani CR, Gianola D (2003) Robust linear mixed models with normal/independent distributions and Bayesian MCMC implementation. Biom J 45(5):573–590CrossRefGoogle Scholar
  72. 72.
    Ducrocq V, Casella G (1996) A Bayesian analysis of mixed survival models. Genet Sel Evol 28(6):505–529CrossRefGoogle Scholar
  73. 73.
    Varona L (1997) Multiple trait genetic analysis of underlying biological variables of production functions. Livest Prod Sci 47:201–209CrossRefGoogle Scholar
  74. 74.
    Forni S, Piles M, Blasco A et al (2009) Comparison of different nonlinear functions to describe Nelore cattle growth. J Anim Sci 87(2):496–506PubMedCrossRefGoogle Scholar
  75. 75.
    Gianola D, Fernando RL (1986) Bayesian methods in animal breeding theory. J Anim Sci 63:217–244Google Scholar
  76. 76.
    Shoemaker JS, Painter IS, Weir BS (1999) Bayesian statistics in genetics - a guide for the uninitiated. Trends Genet 15:354–358PubMedCrossRefGoogle Scholar
  77. 77.
    Blasco A (2001) The Bayesian controversy in animal breeding. J Anim Sci 79(8):2023–2046PubMedGoogle Scholar
  78. 78.
    Beaumont MA, Rannala B (2004) The Bayesian revolution in genetics. Nat Rev Genet 5:251–261PubMedCrossRefGoogle Scholar
  79. 79.
    Yi N, Xu S (2008) Bayesian Lasso for quantitative trait loci mapping. Genetics 179:1045–1055PubMedCrossRefGoogle Scholar
  80. 80.
    Gianola D, de Los Campos G, Hill WG et al (2009) Additive genetic variability and the Bayesian alphabet. Genetics 183(1):347–363PubMedCrossRefGoogle Scholar
  81. 81.
    de Los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E, Weigel K, Cotes J (2009) Predicting quantitative traits with regression models for dense molecular markers and pedigrees. Genetics 182:375–385CrossRefGoogle Scholar
  82. 82.
    Gianola D, Fernando RL, Stella A (2006) Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics 173:1761–1776PubMedCrossRefGoogle Scholar
  83. 83.
    Gianola D, van Kaam JBCHM (2008) Reproducing kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics 178:2289–2303PubMedCrossRefGoogle Scholar
  84. 84.
    Long N, Gianola D, Rosa GJM, Weigel KA, Avendaño S (2007) Machine learning procedure for selecting SNPs in genomic selection: application to early mortality in broilers. J Anim Breed Genet 124(6):377–389PubMedCrossRefGoogle Scholar
  85. 85.
    González-Recio O, Gianola D, Long N, Weigel KA, Rosa GJM, Avendano S (2008) Nonparametric methods for incorporating genomic information into genetic evaluations: an application to mortality in broilers. Genetics 178(4):2305–2313PubMedCrossRefGoogle Scholar
  86. 86.
    Campos G, Gianola D, Rosa GJM (2009) The linear model of quantitative genetics is a reproducing kernel Hilbert spaces regression. J Anim Sci 87:1883–1887CrossRefGoogle Scholar
  87. 87.
    Misztal I, Legarra A, Aguilar I (2009) Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. J Dairy Sci 92:4648–4655PubMedCrossRefGoogle Scholar
  88. 88.
    Gelman A, Carlin JB, Stern HS, Rubin DB (2004) Bayesian data analysis, 2nd edn. Chapman and Hall, LondonGoogle Scholar
  89. 89.
    Bishop CM (2006) Pattern recognition and machine learning. Springer, New YorkGoogle Scholar

Books and Reviews

  1. Chapman AB (1980) General and quantitative genetics. World animal science series. Elsevier, AmsterdamGoogle Scholar
  2. Lange K (1997) Mathematical and statistical methods for genetic analysis. Springer, New YorkGoogle Scholar
  3. Liu BH (1998) Statistical genomics. CRC Press, Boca RatonGoogle Scholar
  4. Mrode R (2005) Linear models for the prediction of animal breeding values, 2nd edn. CAB International, New YorkCrossRefGoogle Scholar
  5. Ott J (1991) Analysis of Human Genetic Linkage. Johns Hopkins, BaltimoreGoogle Scholar
  6. Sham PC (1998) Statistics in human genetics. Arnold, LondonGoogle Scholar
  7. Van Vleck LD (1993) Selection index and introduction to mixed model methods for genetic improvement of animals. CRC Press, Boca RatonGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Departments of Animal Sciences, Biostatistics & Medical Informatics, Dairy ScienceUniversity of WisconsinMadisonUSA