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The marker effect is defined as {widehat{gamma}}_k=arg underset{beta in Omega}{min}left[{left y- Xbeta -sum limits_{k=1}^m{Z}_k{gamma}_kright)}^Tleft y- Xbeta -sum limits_{k=1}^m{Z}_k{gamma}_kright)+lambda sum limits_{k=1}^m|{gamma}_k|right] (5).
The first group assumes that all markers have some effect on the trait of interest and that the variance of each marker effect is equal.
This means that as the number of markers increases, each marker effect is a priori smaller.
When the marker effect is non-null, as in BayesA, the second form of the prior leads to the full-conditional of the marker effect being normal.
If one includes height as well as sex in the model, and because height and sex are correlated, it might be that a 'true' marker effect is attenuated.
However when the prior for the marker effect is specified as a t distribution, its full-conditional is not of a known form.
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The marker effect was explored via growth rate studies in isogenic Vibrio harveyi (Vh) strains altered in quorum sensing on the one hand, and bioluminescence on the other.
The marker effect was assumed to be fixed, while all other effects were assumed to be random.
The significance (P-value) of the F-test for the marker effect was used to select SNPs.
If the number of allele "1" was counted and the sign of the estimated marker effect was positive, then "1" was taken as the favourable allele.
The prior for the probability of marker effect being sampled from the wide distribution was the ratio of the true number of QTL over the number of markers.
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