Effects of data structure on the estimation of covariance functions to describe genotype by environment interactions in a reaction norm model.
Covariance functions have been proposed to predict breeding values and genetic (co)variances as a function of phenotypic within herd-year averages (environmental parameters) to include genotype by environment interaction. The objective of this paper was to investigate the influence of definition of environmental parameters and non-random use of sires on expected breeding values and estimated genetic variances across environments. Breeding values were simulated as a linear function of simulated herd effects. The definition of environmental parameters hardly influenced the results. In situations with random use of sires, estimated genetic correlations between the trait expressed in different environments were 0.93, 0.93 and 0.97 while simulated at 0.89 and estimated genetic variances deviated up to 30% from the simulated values. Non random use of sires, poor genetic connectedness and small herd size had a large impact on the estimated covariance functions, expected breeding values and calculated environmental parameters. Estimated genetic correlations between a trait expressed in different environments were biased upwards and breeding values were more biased when genetic connectedness became poorer and herd composition more diverse. The best possible solution at this stage is to use environmental parameters combining large numbers of animals per herd, while losing some information on genotype by environment interaction in the data.
Calus
Mario P L
MP
Animal Sciences Group, Division Animal Resources Development, PO Box 65, 8200 AB Lelystad, The Netherlands. mario.calus@wur.nl
Bijma
Piter
P
Veerkamp
Roel F
RF
D000704:Analysis of Variance; D000818:Animals; D001947:Breeding / Q000379:methods*; D003198:Computer Simulation; D004777:Environment; D005828:Genetics, Population; D005838:Genotype; D008957:Models, Genetic; D010641:Phenotype; D011156:Population Density; D012107:Research Design