Linear reaction norm models for genetic merit prediction of Angus cattle under genotype by environment interaction

Journal of Animal Science

Volumn 90, Issue 7, 2012, Pages 2130-2141

  Cardoso, F F ^a,b   Tempelman, R J ^c  

^a EMBRAPA CERRADOS   (Brazil)

^b National Counsel of Technological and Scientific Development   (Brazil)

^c MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

Beef cattle; Environmental sensitivity; Genetic evaluation; Genotype by environment interaction; Post weaning body weight gain; Reaction norms

Indexed keywords

ANIMAL; ARTICLE; BRAZIL; CATTLE; ENVIRONMENT; FEMALE; GENETICS; GENOTYPE; MALE; PHYSIOLOGY; STATISTICAL MODEL; WEIGHT GAIN;

ANIMALS; BRAZIL; CATTLE; ENVIRONMENT; FEMALE; GENOTYPE; LINEAR MODELS; MALE; WEIGHT GAIN;

EID: 84863798029     PISSN: 00218812     EISSN: 15253163     Source Type: Journal    
DOI: 10.2527/jas.2011-4333     Document Type: Article

References (51)

1. ASBIA. 2010. Statistical report of semen import, export and marketing. http://www.asbia.org.br/novo/upload/mercado/relato-rio2010.pdf. Accessed May 16,2012.
2. Brooks, S. P., and G. O. Roberts. 1998. Convergence assessment techniques for Markov chain Monte Carlo. Stat. Comput. 8:319-335.
3. Calus, M. P. L., A. F. Groen, and G. de Jong. 2002. Genotype x environment interaction for protein yield in Dutch dairy cattle as quantified by different models. J. Dairy Sci. 85:3115-3123.
4. Calus, M. P. L., L. L. G. Janss, and R. F. Veerkamp. 2006. Genotype by environment interaction for somatic cell score across bulk milk somatic cell count and days in milk. J. Dairy Sci. 89:4846-4857.
5. Cardoso, F. F. 2010. Application of bayesian inference in animal breeding using the Intergen program: Manual of version 1.2. Documents 112, Embrapa Southern Region Animal Husbandry Bagé, RS, Brazil.
6. Cardoso, L. L., J. Braccini Neto, F. F. Cardoso et al. 2011. Hierarchical Bayesian models for genotype x environment interaction estimation via reactions norms applied to post-weaning gain of Hereford cattle. R. Bras. Zootec. 40:294-300.
7. Cardoso, F. F., G. J. M. Rosa, and R. J. Tempelman. 2005. Multiple-breed genetic inference using heavy-tailed structural models for heterogeneous residual variances. J. Anim. Sci. 83:1766-1779.
8. Carroll, R. J., D. Ruppert, L. A. Stefanski, and C. M. Crainiceanu (eds). 2006. Measurement Error in Nonlinear Models: A Modem Perspective, 2nd ed. C&H/CRC Monographs on Statistics & Applied Probability. Chapman and Hall/CRC, Boca Raton, FL.
9. Chib, S., and E. Greenberg. 1995. Understanding the Metropolis-Hastings algorithm. Am. Stat. 49:327-335.
10. Correa, M. B. B., N. J. L. Dionello, and F. F. Cardoso. 2009. Genotype by environment interaction characterization and model comparison for post weaning gain adjustment of Devon cattle via reactionnorms. R. Bras. Zootec. 38:1468-1477.
11. Correa, M. B. B., N. J. L. Dionello, and F. F. Cardoso. 2010. Genetic evaluation of Devon Cattle using a reaction norms model. R. Bras. Zootec. 39:128-133
12. Costa, C. N., R. W. Blake, E. J. Poliak et al. 2000. Genetic analysis ofHolstein cattle populations in Brazil and the United States. J. DairySci. 83:2963-2974.
13. de Alencar, M. M., A. D. Mascioli, and A. R. de Freitas. 2005. Evidences of genotype x environment interaction for growth traits in beef cattle. R. Bras. Zootec. 34:489-495.
14. Deeb, N., and A. Cahaner. 2001. Genotype-by-environment interaction with broiler genotypes differing in growth rate. 1. The effects of high ambient temperature and naked-neck genotype on lines differing in genetic background. Poult. Sci. 80:695-702.
15. de Jong, G. 1995. Phenotypic plasticity as a product of selection in a variable environment. Am. Nat. 145:493.
16. de Jong, G., and P. Bijma. 2002. Selection and phenotypic plasticity in evolutionary biology and animal breeding. Livest. Prod. Sci. 78:195-214.
17. de Mattos, D., J. K. Bertrand, and I. Misztal. 2000. Investigation of genotype x environment interactions for weaning weight for Herefords in three countries. J. Anim. Sci.78:2121-2126.
18. Donoghue, K. A., D. J. Johnston, H.-U. Graser, and B. Tier. 2007. AGBU's approach to across-country genetic evaluation for beef cattle. Interbull Bull. 37:12-15.
19. Espasandín, A. C., and J. I. Urioste. 2005. Sustainability of genetic resources for beef cattle production on pastures: Considering the interaction between genotypes and environments. Agrociencia (Uruguay) 9:569-578.
20. Falconer, D. S. 1952. The problem of environment and selection. Am. Nat. 86:293-298.
21. Falconer, D. S. 1990. Selection in different environments: effects on environmental sensitivity (reaction norm) and on mean performance. Genet. Res. 56:57-70.
22. Falconer, D. S., and T. F. C. Mackay. 1996. Introduction to Quantitative Genetics. 4 ed. Longman Group Ltd, Harlow, UK.
23. Fernandez, C., and M. F. J. Steel. 1998. On Bayesian modeling of fat tails and skewness. J. Am. Stat. Assoc. 93:359-371.
24. Geweke, J. 1992. Evaluating the Acuracy of Sampling-based Approaches to the Calculation of Posterior Moments (with Discussion). Pages 164-193 in Bayesian Statistics No. 4. J. O. Berger, J. M. Bernardo, A. P. Dawid and S. A. F. M. ed. Oxford University Press, Oxford, UK.
25. Goddard, M. E., and B. J. Hayes. 2009. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat. Rev. Genet. 10:381-391.
26. Hayes, B. J., P. J. Bowman, A. J. Chamberlain, and M. E. Goddard. 2009. Invited review: Genomic selection in dairy cattle: progress and challenges. J. Dairy Sci. 92:1313-1313.
27. Kirkpatrick, M., D. Lofsvold, and M. Bulmer. 1990. Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124:979-993.
28. Kizilkaya, K., and R. J. Tempelman. 2005. A general approach to mixed effects modeling of residual variances in generalized linear mixed models. Genet. Sel. Evol. 37:31-56.
29. Knap, P. W., and G. Su. 2008. Genotype by environment interaction for litter size in pigs as quantified by reaction norms analysis. Animal 2:1742-1747.
30. Kolmodin, R., and P. Bijma. 2004. Response to mass selection when the genotype by environment interaction is modelled as a linear reaction norm. Genet., Sel., Evol. 36:435-454.
31. Kolmodin, R., E. Strandberg, H. Jorjani, and B. Danell. 2003. Selection in the presence of a genotype by environment interaction: response in environmental sensitivity. Anim. Sci. 76:375385.
32. Kolmodin, R., E. Strandberg, P. Madsen, J. Jensen, and H. Jorjani. 2002. Genotype by environment interaction in Nordic dairy cattle studied using reaction norms. Acta Agric. Scand. Sect. A. Anim. Sci. 52:11-24.
33. Lee, D. H., and J. K. Bertrand. 2002. Investigation of genotype x country interactions for growth traits in beef cattle. J. Anim. Sci. 80:330-337.
34. Lillehammer, M., M. E. Goddard, H. Nilsen et al. 2008. Quantitative trait locus-by-environment interaction for milk yield traits on Bos taurus autosome 6. Genetics 179:1539-1546.
35. Lillehammer, M., J. Odegard, and T. H. E. Meuwissen. 2007. Random regression models for detection of gene by environment interaction. Genet., Sel., Evol. 39:105-121.
36. Lynch, M., and B. Walsh. 1998. Genetics andAnalysis of Quantitative Traits. 1st ed. Sinauer Associates, Inc., Sunderland, MA.
37. Mathur, P. K., and P. Horst. 1994. Genotype by environment interactions in laying hens based on relationship between breeding values of sires in temperate and tropical environments. Poult. Sci. 73:1777-1784.
38. Mattar, M., L. O. C. Silva, M. M. Alencar, and F. F. Cardoso. 2011. Genotype by environment interaction for long-yearling weight in Canchim cattle quantified by reaction norm analysis. J. Anim. Sci. 89:2349-2355.
39. Meyer, K. 1998. Estimating covariance functions for longitudinal data using a random regression model. Genet., Sel., Evol. 30:221-240.
40. Montaldo, H. H., S. G. Nunez-Soto, F. J. Ruiz-Lopez, and H. Castillo-Juarez. 2009. Selection response for milk production in conventional production systems in Mexico, using genetic evaluations of Holstein sires from Canada and the United States. J. Dairy Sci. 92:5270-5275.
41. Mulder, H. A., R. F. Veerkamp, B. J. Ducro, J. A. M. van Arendonk, and P. Bijma. 2006. Optimization of dairy cattle breeding programs for different environments with genotype by environment interaction. J. Dairy Sci. 89:1740-1752.
42. Ojango, J. M. K., and G. E. Pollott. 2002. The relationship between Holstein bull breeding values for milk yield derived in both the UK and Kenya. Livest. Prod. Sci. 74:1-12.
43. Pegolo, N. T., H. N. Oliveira, L. G. Albuquerque, L. A. F. Bezerra, and R. B. Lobo. 2009. Genotype by environment interaction for 450-day weight of Nelore cattle analyzed by reaction norm models. Genet. Mol. Biol. 32:281-287.
44. Pollott, G. E., and J. C. Greeff. 2004. Genotype x environment interactions and genetic parameters for fecal egg count and production traits of Merino sheep. J. Anim. Sci. 82:2840-2851.
45. Robertson, A. 1959. The sampling variance of the genetic correlation coefficient. Biometrics 15:469-485.
46. Roso, V. M., and F. S. Schenkel. 2006. AMC-A computer program to assess the degree of connectedness among contemporary groups. Pages 26-27 in 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil.
47. Schaeffer, L. R. 1994. Multiple-country comparison of dairy sires. J. Dairy Sci. 77:2671-2678.
48. Schaeffer, L. R. 2004. Application of random regression models in animal breeding. Livest. Prod. Sci. 86:35-45.
49. Shariati, M. M., G. Su, P. Madsen, and D. Sorensen. 2007. Analysis of milk production traits in early lactation using a reaction norm model with unknown covariates. J. Dairy Sci. 90:5759-5766.
50. Spiegelhalter, D. J., N. G. Best, B. P. Carlin, and A. van derLinde. 2002. Bayesian measures of model complexity and fit. J. R. Stat. Soc. Ser. B-Stat. Methodol. 64:583-616.
51. Su, G., P. Madsen, M. S. Lund et al. 2006. Bayesian analysis of the linear reaction norm model with unknown covariates. J. Anim. Sci. 84:1651-1657.