These include the following LPD, heritability, variance components, parameter estimates, cell means, posterior probabilities and Bayes factors.
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Over the past few decades there has been considerable development of statistical methodology to map QTL for complex traits.
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Note that design matrices, XtG, XtGG, XtGE, have a subscript t corresponding to the tth trait which allows each trait to have a separate genetic model which main advantage of the QTL SUR model.
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Another popular design is the intercross or F2 design where the progenies of F1 are mated with themselves or each other to generate the F2 progenies having three possible genotypes HH, LH and LL.
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These also comprised changes in the scale and nature of the physical surroundings as reflected in the architectural endeavours and traditions of late Epipalaeolithic and Neolithic groups.
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One simple summary of the MCMC sample is the posterior profile of a particular trait, or the posterior probability of a QTL at each locus for the trait.
In the plot, the actual locations of possible QTL are jittered slightly in order to give a sense of the density of putative QTL in the vicinity of each marker.
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First, the chief advantage is in the detection of the phenomenon of pleiotropy, in which action at a single locus leads to variation in a number of traits.
As shown in Figure 4, the 2log(BF) profile of the first trait can be obtained for all types of effect, namely, main effect, epistatic and GxE interactions.
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QTL involved in models (shown as a broken blue line) against the corresponding posterior probabilities (shown as a histogram).
These plots are useful as an initial tool for examining the evidence in favor of multiple QTL models and in determining the locations of QTL.
Since each iteration of the MCMC chain represents a single model for a particular trait; therefore, we can explore the history of the MCMC chain by plotting time series for relevant model features.
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For pairs of values along the horizontal axis of this plot, the member of the pair with a larger Bayes factor ratio should be interpreted as more likely.
The genotypes at these putative loci are unobserved but the probabilities can be calculated from the observed marker data using the multipoint method (Jiang and Zeng).
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Although it might seem paradoxical to compute BF for 1 vs 0 QTL in a multiple QTL mapping framework, by virtue of model averaging we can actually control for QTL elsewhere in the genome.
One can estimate the genotypes at these candidate loci from the genotypes of the flanking markers and the probability of a recombination event happening between the marker and the putative QTL.
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