نتایج جستجو برای: including i genomic best linear unbiased prediction gblup

تعداد نتایج: 2908604  

2014
Jørgen Ødegård Thomas Moen Nina Santi Sven A. Korsvoll Sissel Kjøglum Theo H. E. Meuwissen

Reliability of genomic selection (GS) models was tested in an admixed population of Atlantic salmon, originating from crossing of several wild subpopulations. The models included ordinary genomic BLUP models (GBLUP), using genome-wide SNP markers of varying densities (1-220 k), a genomic identity-by-descent model (IBD-GS), using linkage analysis of sparse genome-wide markers, as well as a class...

Journal: :Genetics 2000
P Bijma J A Woolliams

Predictions for the rate of inbreeding (DeltaF) in populations with discrete generations undergoing selection on best linear unbiased prediction (BLUP) of breeding value were developed. Predictions were based on the concept of long-term genetic contributions using a recently established relationship between expected contributions and rates of inbreeding and a known procedure for predicting expe...

Journal: :Frontiers in Plant Science 2023

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity quality compounds view climate change conditions. global need for plant-based proteins has increased demand seeds with a that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as standard approach ag...

2013
Aaron J. Lorenz

Allocating resources between population size and replication affects both genetic gain through phenotypic selection and quantitative trait loci detection power and effect estimation accuracy for marker-assisted selection (MAS). It is well known that because alleles are replicated across individuals in quantitative trait loci mapping and MAS, more resources should be allocated to increasing popu...

رحیمی میانجی, قدرت, غفوری کسبی, فرهاد, نجاتی جوارمی, اردشیر, هنرور, محمود,

One of the most important issues in genomic selection is using a decent method for estimating marker effects and genomic evaluation. Recently, machine learning algorithms which are members of non-parametric and non-linear methods have been extended to genomic evaluation. One of these methods is Random Forest (RF) on which this research was focused. Important parameters in RF algorithm are the n...

‎In this paper‎, ‎we have dealt with the distribution theory of concomitants of order statistics arising from Farlie-Gumbel-Morgenstern bivariate Lomax distribution‎. ‎We have discussed the estimation of the parameters associated with the distribution of the variable Y of primary interest‎, ‎based on the ranked set sample defined by ordering the marginal observations...

2007

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 ...

Journal: :Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie 2011
M E Goddard B J Hayes T H E Meuwissen

Estimated breeding values (EBVs) using data from genetic markers can be predicted using a genomic relationship matrix, derived from animal's genotypes, and best linear unbiased prediction. However, if the accuracy of the EBVs is calculated in the usual manner (from the inverse element of the coefficient matrix), it is likely to be overestimated owing to sampling errors in elements of the genomi...

2012
Xiaochen Sun Long Qu Dorian J. Garrick Jack C. M. Dekkers Rohan L. Fernando

Prediction accuracies of estimated breeding values for economically important traits are expected to benefit from genomic information. Single nucleotide polymorphism (SNP) panels used in genomic prediction are increasing in density, but the Markov Chain Monte Carlo (MCMC) estimation of SNP effects can be quite time consuming or slow to converge when a large number of SNPs are fitted simultaneou...

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