the objective of this study was to compare six statistical methods for prediction of genomic breedingvalues for traits with different genetic architecture in term of gene effects distributions and number ofquantitative traits loci (qtls). a genome consisted of 500 bi-allelic single nucleotide polymorphism(snp) markers distributed over a chromosomes with 100 cm length was simulated. three differ...

genomic selection (gs) is a tool for prediction of breeding values for quantitative traits. for a successful application of gs, accuracy of predicted genomic breeding value (gebv) is a key issue to consider. here we investigated the accuracy of gebv in 345 genotyped iranian holstein cattle. the study was performed on milk, fat, protein yield and somatic cell count. four methods g-blup, bayes b,...

Genomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype, and expressed late in the life-cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of...

Genomic selection can increase genetic gain per generation through early selection. Genomic selection is expected to be particularly valuable for traits that are costly to phenotype and expressed late in the life cycle of long-lived species. Alternative approaches to genomic selection prediction models may perform differently for traits with distinct genetic properties. Here the performance of ...

Word-posi t ionindependent and word -pos i t i on dependent n-gram p r o b a b i l i t i e s were est imated from a la rge Engl ish language corpus. A t e x t r e c o g n i t i o n problem was s imu la ted , and using the est imated n-gram p r o b a b i l i t i e s , four experiments were conducted by the f o l l o w i n g methods of c l a s s i f i c a t i o n : w i thou t contex tua l i n f o...

Following a Bayesian statistical inference paradigm, we provide an alternative methodology for analyzing a multivariate logistic regression. We use a multivariate normal prior in the Bayesian analysis. We present a unique Bayes estimator associated with a prior which is admissible. The Bayes estimators of the coefficients of the model are obtained via MCMC methods. The proposed procedure...

.................................................................................................................................. iii ACKNOWLEDGMENTS ............................................................................................................. iv LIST OF TABLES .........................................................................................................................