Properties of threshold model predictions.

Abstract

Estimation of genetic parameters and accuracy of threshold model genetic predictions were investigated. Data were simulated for different population structures by using Monte Carlo techniques. Variance components were estimated by using threshold models and linear sire models applied to untransformed data, logarithmically transformed data, and transformation to Snell scores. Effects of number of categories (2, 5, and 10), incidence of categories (extreme, moderate, and normal), heritability in the underlying scale (.04, .20, and .50), and data structure (unbalanced and balanced) on accuracy of genetic prediction were investigated. The real importance of using a threshold model was to estimate genetic parameters. An expected heritability of .20 was estimated to be .22 and .10 by a threshold model and a linear model, respectively. Accuracy increased significantly with a larger number of categories, a more normal distribution of incidences, increased heritability, and more balanced data. Even threshold models were shown to be more efficient with more than two categories (e.g., binomial). Transformation of scale did not accomplish the purpose intended.