Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic be...

Two generations of selection on restricted BLUP breeding values were applied in an experiment with laying hens. Selection had been on phenotype of income minus feed cost (IFC) between 21 and 40 wk of age in the previous five generations. The restriction of no genetic change in egg weight was included in the EBV for power-transformed IFC (i.e., IFCt, with t-values of 3.7 and 3.6 in the two gener...

The second step of a procedure to partially circumvent the voluminous calculations for some BLUP (Best Linear Unbiased Prediction) computing algorithms for genetic evaluation is presented. In addition, the procedure allows timely evaluations of each contemporary group. This procedure is pertinent especially for polytocous species such as swine and poultry, for which the occurrence of full-sib f...

Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information Murrah buffalo; (2) evaluate the performance of prediction for milk-related traits using single- and multitrait random regression models (RRM) single-step best linear unbiased approa...

Misspecifications in econometric models can result in misestimated coefficients.An improved method for specifying econometric models is presented. The mean square error of an empirical best linear unbiased predictor of an individual drawing for the dependent variable of an improved model is derived. These ideas are illustrated using certain misspecified and improved models of the demand for gas...

Protein phosphorylation is one of the most pervasive post-translational modifications, regulating diverse cellular processes in various organisms. As mass spectrometry-based experimental approaches for identifying phosphorylation events are resource-intensive, many computational methods have been proposed, in which phosphorylation site prediction is formulated as a classification problem. They ...

This paper presents a new method for regression model prediction in an uncertain environment. In practical engineering problems, in order to develop regression or ANN model for making predictions, the average of set of repeated observed values are introduced to the model as an input variable. Therefore, the estimated response of the process is also the average of a set of output values where th...

For the well-known Fay–Herriot small area model, standard variance component estimation methods frequently produce zero estimates of the strictly positive model variance. As a consequence, an empirical best linear unbiased predictor of a small area mean, commonly used in small area estimation, could reduce to a simple regression estimator, which typically has an overshrinking problem. We propos...