If the goal is genetic improvement and the underlying genetic architecture is known to be additive, then parametric GS methods will provide better predictions for selection.
In this paper, we hypothesise that the use of a nonlinear method (BayesR), combined with a multi-breed (Holstein/Jersey) reference population will map causative mutations with more precision than GBLUP and this, in turn, will increase the accuracy of genomic predictions for selection candidates that are less related to the reference animals.
It makes use of genotypic information to make predictions used for selection decisions.
Wrong assumptions could not only lead to biased estimates of genetic parameters, but also produce wrong predictions for the selection response.
However, it achieves high accuracies of prediction only for selection candidates closely related to the plant material evaluated in field trials, resulting in a rather pessimistic prognosis for distantly related material.
To check model fit, we consider the posterior predictives (Gelman, Carlin, Stern, & Rubin, 2004)—that is, model predictions for card selections based on samples from the joint posterior distribution.
Our results suggest that accurate predictions of selection response for disease risk and severity require a thorough understanding of the structure of genetic variation, i.e., of the source of genetic variation and the shape of the corresponding distributions.
This enhances the potential for improving the accuracy of estimates of genetic values, thus increasing the genetic progress attainable when these predictions are used for selection purposes in lieu of pedigree-based predictions.
The four methods which use information from all SNP namely RR-BLUP, Bayes-R, PLSR and SVR generate similar accuracies of MBV prediction for genomic selection, and their use in the selection of immediate future generations in dairy cattle will be comparable.
Our study has implications for the biological interpretation of field data, and for the prediction of selection response from breeding for nematode resistance.
A nonrandom relationship between body size and island area, as seen here, is an explicit prediction of selection for optimal body size due to indirect island area effects on resource availability and community composition [4], [16], [17].
