Best linear unbiased prediction to predict category frequencies of future progeny of a sire for type traits scored in mutually exclusive categories is described. The method accounts for automatic covariances among categories and is comparable to prediction for multiple traits. The method does not require linearity of measurements and also allows nonlinear economic values to be assigned to each ...

Genomic selection is an upgrading form of marker-assisted selection for quantitative traits, and it differs from the traditional marker-assisted selection in that markers in the entire genome are used to predict genetic values and the QTL detection step is skipped. Genomic selection holds the promise to be more efficient than the traditional marker-assisted selection for traits controlled by po...

The recent discovery of a major gene for rapid postweaning growth has reinforced the hypothesis that other quantitative continuous traits may be influenced by single genes of large effect. However, most methods for the detection of such genes rely on the discovery of multimodality in the population frequency distribution. The complicating effects of environment and artificial selection make the...

Genetic evaluation by best linear unbiased prediction (BLUP) requires modeling genetic means, variances, and covariances. This paper presents theory to model means, variances, and covariances in a multibreed population, given marker and breed information, in the presence of gametic disequilibrium between the marker locus (ML) and linked quantitative trait locus (MQTL). Theory and algorithms are...

Despite important advances from Genome Wide Association Studies (GWAS), for most complex human traits and diseases, a sizable proportion of genetic variance remains unexplained and prediction accuracy (PA) is usually low. Evidence suggests that PA can be improved using Whole-Genome Regression (WGR) models where phenotypes are regressed on hundreds of thousands of variants simultaneously. The Ge...