An Approximation of the Minimum-Variance Estimator of Heritability Based on Variance Component Analysis

AUTOR(ES)

Grossman, M.

RESUMO

An approximate minimum-variance estimate of heritability (h2) is proposed, using the sire and dam components of variance from a hierarchical analysis of variance. The minimum sampling variance is derived for unbalanced data. Optimum structures for the estimation of h2 are given for the balanced case. The degree to which ĥ2 is more precise than the equally weighted estimate ĥ2S+D is a function of the size and structure of the sample used. However, computer simulation reveals that ĥ2 has less desirable behavior than ĥ2S+D. An iterative procedure improved the estimation of h2, especially in small populations, when those values of ĥ2S or ĥ2D outside the range of the parameter were constrained to zero or unity.

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