A. Reverter et al., A BOOTSTRAP APPROACH TO FROM CONFIDENCE-REGIONS FOR GENETIC-PARAMETERS METHOD-R ESTIMATES, Journal of animal science, 76(9), 1998, pp. 2263-2271
Confidence regions (CR) for heritability (h(2)) and fraction of varian
ce accounted for by permanent environmental effects (c(2)) from Method
R estimates were obtained from simulated data using a univariate, rep
eated measures, full animal model, with 50% subsampling. Bootstrapping
techniques were explored to assess the optimum number of subsamples n
eeded to compute Method R estimates of h2 and c2 with properties simil
ar to those of exact estimators. One thousand estimates of each parame
ter set were used to obtain 90, 95, and 99% CR in four data sets inclu
ding 2,500 animals with four measurements each. Two approaches were ex
plored to assess CR accuracy: a parametric approach assuming bivariate
normality of h2 and c2 and a nonparametric approach based on the sum
of squared rank deviations. Accuracy of CR was assessed by the average
loss of confidence (LOSS) by number of estimates sampled (NUMEST). Fo
r NUMEST = 5, bootstrap estimates of h(2) and c(2) were within 10(-3)
of the asymptotic ones. The same degree of convergence in the estimate
s of SE was achieved with NUMEST = 20. Correlation between estimates o
f h(2) and c(2) ranged from -.83 to -.98. At NUMEST < 10, the nonparam
etric CR were more accurate than parametric CR. However, with the para
metric CR, LOSS approached zero at rate NUMEST-1. This rate was an ord
er of magnitude larger for the nonparametric CR. These results suggest
ed that when the computational burden of estimating genetic parameters
limits the number of Method R estimates that can be obtained to, say,
10 or 20, reliable CR can still be obtained by processing Method R es
timates through bootstrapping techniques.