Sentence examples for a breeding context from inspiring English sources

Due to the latter two properties, quantitative resistances are today considered more useful in a breeding context than qualitative resistances.

Although in a breeding context this type of information may be available when collecting data to implement genomic prediction and subsequent selection, estimates of the variance components may not in themselves have value beyond what is obtained from the marker trait associations.

Sometimes, particularly in an animal breeding context, an indicator trait is used to describe the impact of infection or disease upon an individual, for example somatic cell count in the milk of lactating ruminants with mastitis [10].

The approach of using a training set that combines different groups also has been studied in a plant breeding context for genomic prediction of oat (Asoro et al. 2011) and barley (Lorenz et al. 2012).

Populations of paternal half-sib families were simulated, resembling a simplified population structure in an animal breeding context with large half-sib offspring groups per sire like in dairy cattle.

Multi-trait extensions of the GBLUP method (i.e., genomic best linear unbiased prediction), which make a strong assumption of uniform variances and covariances of marker effects across the genome (Lund et al. 2014), are straight-forward to implement and their performance was evaluated in a dairy cattle breeding context by Karoui et al. (2012) and Olson et al. (2012).

In the animal breeding context it is well known that a BLUP approach for the model y = Wβ + Zu + g(X) + e is equivalent to solving the mixed model equations (MME) (2) [ W T W W T Z W T Z T W Z T Z + σ e 2 σ u 2 A − 1 Z T W Z I + σ e 2 K ν, h, σ K − 1 ] ⋅ [ β ˆ u ˆ α ˜ ] = [ W T y Z T y y ] for given variance components estimated, e.g., by ML.

In forest tree breeding context, GS has the ability to predict the phenotype for selecting elite genotypes at early age and developmental stage, thus substantially shortening the breeding cycle and increasing the selection differential, ultimately raising the genetic gain per unit time [ 13- 16].

The Bayes factor developed by Verdinelli and Wasserman [ 11] from generalization of the Savage-Dickey density ratio, and adapted to the animal breeding context by García-Cortés et al. [ 18] and Varona et al. [ 12], has been easily applied to check heterogeneous residual variances in gene expression analyses when two groups of treatments are compared.

Our results support the idea that ANN with Bayesian regularization can act as universal approximators of linear or non-linear functions of interest in breeding contexts.

They may prove to be valuable in some plant diversity, selection and breeding contexts, allowing larger samples and complementing use of DNA markers.