Sentence examples for breeding values for animals from inspiring English sources

To investigate the ability of the models to predict breeding values for animals with records for the two traits, only one, or none of the traits, two scenarios were considered differing in the number of animals that had phenotypes available for each of the traits (Table 1).

This approach, which was first proposed by Meuwissen et al. [ 1], uses a reference population (usually consisting of progeny-tested bulls) with both genotypes and phenotypes to estimate marker effects and then uses these estimates to predict breeding values for animals without phenotypes.

As in Hayes et al. [ 14], we assumed a model where y is a vector of phenotypes, μ is the mean, 1 n is a vector of 1s, Z is a design matrix allocating records to breeding values, g is a vector of breeding values for animals in the reference set and the test set and e is a vector of random normal deviates ~.

In contrast, gBLUP breeding values, for animals that had no pedigree relationship with animals in the reference data set, allowed substantial accuracy.

Dense genotypes (38 453 SNPs) on 3250 elite breeding pigs were combined with phenotypes for growth rate (2668 records), lean meat percentage (2618), weight at three weeks of age (7387) and number of teats (5851) to estimate breeding values for all animals in the pedigree (8187 animals) using the aforementioned relationship matrices.

Full data set (n = 338,346), ste = standard error Subset of genotyped animals (n = 1,919), ste = standard error Estimates of breeding values for genotyped animals were on average similar regardless the choice of G. Table 4 presents correlations between breeding values obtained with different relationship matrices.

Estimating breeding values for genotyped animals and absorbing non-genotyped progeny into their equations can make full use of all available data.

In the same analysis Best Linear Unbiased Prediction (BLUP) breeding values for each animal was produced.

Mixed models play an important role in the prediction of breeding values for plants and animals.

Breeding values for the validation animals were predicted using phenotypes of the training set, which were pre-corrected for hatch week.

Breeding values for the n animals in generation t = 1002 were estimated using the SNP marker information and the phenotypes in generation t = 1001, and compared to the true breeding values (TBV) in generation t = 1002.