Sentence examples for genetic random from inspiring English sources

In quantitative genetics, Markov chain Monte Carlo (MCMC) methods are indispensable for statistical inference in non-standard models like generalized linear models with genetic random effects or models with genetically structured variance heterogeneity.

A particular challenge for MCMC applications in quantitative genetics is to obtain efficient updates of the high-dimensional vectors of genetic random effects and the associated covariance parameters.

The paper reports the result of an empirical study of algorithm performance and result-similarity for Hill Climbing, Genetic, Random Search and Greedy Algorithms applied to a set of 12 C programs.

Including genotype as an additive genetic random effect to the base model improved the model significantly (LRT = 22.26, P < 0.001).

The model specifies that a breeding value is the sum of a genomic and a polygenic genetic random effect, where genomic genetic random effects are correlated with a genomic relationship matrix constructed from markers and the polygenic genetic random effects are correlated with the usual relationship matrix.

It is also interesting to note that the estimation errors of the environmental random effects variance were smaller than the estimation errors of the genetic random effects variance.

The reason for fitting cage*sex as a non-genetic random effect, was to test whether social interactions in mink depend on sex.

Step one consisted of deriving average daughter performance corrected for fixed and non-genetic random effects and for genetic effects of the dams (daughter yield deviations, DYD).

Regarding the non-genetic random effects, the cage*sex effect fitted the data better than the cage effect (except for Body BMS).

Table  5 shows the estimated variance components obtained with the classical model that included direct genetic effects only, but accounted for both cage and cage*sex as non-genetic random effects (Model 3).

To avoid use of overlapping information between the reference and validation animals [ 20], we based the validation on the own phenotype of genotyped validation animals, adjusted for fixed effects and non-genetic random effects.