Sentence examples for multilocus association models from inspiring English sources

Additionally, multilocus association models with different shrinkage or variable selection mechanisms may be able to cope with different amount of oversaturation.

A relatively recent contender for the BLUP-type of model in the genomic selection field is to apply simultaneous estimation and variable selection or variable regularization to multilocus association models (e.g., Meuwissen et al. 2001; Xu 2003).

Furthermore, the threshold approach for censored observations has been considered by Broman (2003), within BLUP context by Sorensen et al. (1998), and with multilocus association models by Sillanpää and Hoti (2007) and Iwata et al. (2009).

Multilocus association models of binary and ordinal traits have been considered by Hoti and Sillanpää (2006), Iwata et al. (2009), González-Recio et al. (2009), González-Recio and Forni (2011), and Wang et al. (2013).

Our example analyses suggest that the use of the extra information present in an ordered categorical or censored Gaussian data set, instead of dichotomizing the data into case-control observations, increases the accuracy of genomic breeding values predicted by Bayesian multilocus association models or by Bayesian genomic best linear unbiased prediction.

Our example analyses show that using the extra classes and the uncensored observations present in an ordered categorical and a censored Gaussian data set, instead of dichotomizing the data into case-control observations, increases the accuracy of genomic breeding values predicted by Bayesian multilocus association models or by Bayesian G-BLUP.

The fully conditional posterior densities for the multilocus association model (1) parameters are as follows.

The derivations of the fully conditional posterior densities of the multilocus association model are presented in the Appendix A1.

An advantage of the G-BLUP over the multilocus association model is that no preselection of the markers is needed.

Contrary to G-BLUP, a multilocus association model uses the marker information directly by assigning different, possibly zero, effects to the marker genotypes.

With the 100 Gaussian QTL-MAS data replicates, the multilocus association model produces an average correlation 0.89 whereas the G-BLUP produces an average correlation 0.80.