A bootstrap approach to confidence regions for genetic parameters from Method R estimates.

Abstract

Confidence regions (CR) for heritability (h2) and fraction of variance accounted for by permanent environmental effects (c2) from Method R estimates were obtained from simulated data using a univariate, repeated measures, full animal model, with 50% subsampling. Bootstrapping techniques were explored to assess the optimum number of subsamples needed to compute Method R estimates of h2 and c2 with properties similar to those of exact estimators. One thousand estimates of each parameter set were used to obtain 90, 95, and 99% CR in four data sets including 2,500 animals with four measurements each. Two approaches were explored 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). For NUMEST = 5, bootstrap estimates of h2 and c2 were within 10(-3) of the asymptotic ones. The same degree of convergence in the estimates of SE was achieved with NUMEST = 20. Correlation between estimates of h2 and c2 ranged from -.83 to -.98. At NUMEST < 10, the nonparametric CR were more accurate than parametric CR. However, with the parametric CR, LOSS approached zero at rate NUMEST(-1). This rate was an order of magnitude larger for the nonparametric CR. These results suggested 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 estimates through bootstrapping techniques.