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Prediction of single SNP marker effects was carried out using BayesR [ 11].
Peaks indicating QTL were identified but were not very distinctive because a common prior for the marker effects was used.
A gain in significance of marker effects was achieved through MI, but only for rare cases when missing data were <45%.
In all cross-validation approaches, data sets were divided into an estimation set (ES) that was used to estimate marker effects, and a test set (TS), in which the predictive ability (Pearson correlation rMP) between observed BLUEs and the genotypic values predicted based on the determined marker effects was calculated to provide a measure of the accuracy of prediction [ 11].
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An alternative to estimating GEBVs by summing up all the marker effects, is to estimate GEBVs directly within the framework of mixed model equations (MME).
In the BRR, marker effects are IID normal random variables.
Family main effects were always included and marker effects were nested in families.
The marker effects are obtained by solving the optimization problem, where is a regularization parameter.
Furthermore, when π is close to 1, the sampled value for most marker effects is null.
In multilevel models, specific marker effects are estimated for each population.
Permutated marker effects were obtained from the posterior means of a 250,000 iteration chain.
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