Sentence examples for marker effects have from inspiring English sources

Several analytical approaches based on different assumptions with regard to the marker effects have been proposed for GS [ 8, 18].

Assume that the marker effects have a Gaussian distribution where I m is an m × m identity matrix.

Thus, given species-specific considerations and the potential variability of the reforestation landscape, it appears important to determine whether the estimated marker effects have accurate predictive value in the different reforestation environments.

With this approach, used in models BayesA, BayesB, and BL, the fully conditional densities of marker effects as well as those of the conditional variances of marker effects have closed forms.

This is predicated on having a large reference population, where CH4 emission levels can be measured cheaply and genome wide DNA marker effects have been estimated, to establish the prediction equation for marker effects.

The hypothesis tested by this test is that marker effects have the same pattern for the two traits in the case of pleiotropism (H0) but different patterns in the case of close linkage (H1).

All three models had almost identical accuracies except when the number of observations exceeded the number of markers and the marker effects had a non-null mean.

However, the reduction during the first generation after selection had stopped was not as large as for EVERY-2GEN, probably because more generations of information on marker effects had built up.

Methods such as Bayes A and Bayes B assume that the variance of marker effects has an a priori inverse χ distribution (Meuwissen et al. 2001) that produces shrinkage as well as variable selection.

The second form of the prior, however, is more convenient because it results in the full-conditional for the marker effect having a normal distribution.

This prior density yielded shrunken estimates of marker effects and has been successfully used for WGP (de los Campos et al. 2009).