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In general, the marker effects showed excellent mixing.
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Although the focus in the development of these models has been to estimate breeding values rather than to identify QTL, the estimated marker effects show potential use for QTL mapping [ 11].
However, we found that most markers for systemic effects showed significant changes only in the 5m group, not in the 1w group, whereas signs of strong pulmonary inflammation were evident in the 1w exposure group.
The variances are assumed to follow a scaled inversed χ distribution with degrees of freedom νa and scale parameter S a 2. The unconditional distribution of the marker effects can be shown to follow a t-distribution with mean zero (Sorensen and Gianola 2002).
Linkage studies have shown that marker effects are generally limited to the family (genetic background) in which they were estimated [ 48] because the full variation of the causal loci in the population is not sampled.
In Figure S4, the consistency of marker effects from (CC) to (MI) is shown.
Recall that we have shown that the posterior mean of the marker effects does not depend on the pseudo prior.
Gianola [ 5] has shown that RR does not lead to uniform shrinkage of marker effects.
The simulated (true) QTL effects and the marker effects estimated from RRBLUP and BayesB from one random replicate of the standard scenario are shown in Figure 3.
This shows the importance of having large numbers of phenotypic records to accurately estimate marker effects.
Boxplots of the 12 most effective marker alleles show the effects of the allele's presence/absence or increasing dosage on the tuber reducing sugar content after 12 weeks' cold storage.
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