Sentence examples for multiple trait models from inspiring English sources

Multiple trait models were fit to estimate genetic covariances among traits.

Because the proposed approach searches for minimal causal structures, applying the retrieved structures to fit a recursive SEM would result in parsimonious modeling of joint probability distributions derived from multiple trait models.

Structural Equation Models (SEM) can be used to study recursive and simultaneous relationships among phenotypes in multivariate systems such as genetical genomics, system biology, and multiple trait models in quantitative genetics.

As discussed by [ 16], in the classical genetic evaluation scenario, breeding values of candidate individuals are predicted by fitting multiple trait models (MTM), which neglect the causal network that influences phenotypic traits.

In animal breeding and quantitative genetics, relationships among phenotypic traits are traditionally studied via probabilistic relationships between them, using standard Multiple Trait Models (MTM) - see, for example, [ 1, 2].

Hence, SEM can produce an interpretation of relationships among traits which differs from that obtained with traditional multiple trait models, in which all relationships are represented by symmetric linear associations among random variables, such as covariances and correlations.

By trend, multiple trait model results (based on created discrete herd classes) confirmed the random regression estimates.

M: Montbéliarde; N Normande; H: Holstein; RRM: Random Regression Model MTM: Multiple Trait Model; rg: genetic correlation between breeds.

In addition, a QTL analysis with a multiple trait model combining this 5 gene-set and AF allowed us to refine the QTL region.

In the structured competition, normal scores were used first in a single trait model whatever category of event, and second, with a multiple trait model, i.e., one trait for each category of event.

With the multiple trait model, as in the single trait model, the repeatability was always underestimated, and the differences of average values of horses in each level were still underestimated.