Using the expectation or the distribution of the identity by descent for mapping quantitative trait loci under the random model.

We examine the ability of four implementations of the random model to map quantitative trait loci (QTLs). The implementations use either the expectation or the distribution of the identity-by-descent value at a putative QTL and either a 2 x 1 vector of sib-pair traits or their scalar difference. When the traits of both sibs are used, there is little difference between the expectation and distribution methods, while the expectation method suffers in both precision and power when the difference between traits is used. This is consistent with the prediction that the difference between the expectation and distribution methods is inversely proportional to the amount of information available for mapping. We find, though, that the amount of information must be very low for this difference to be noticeable. This is exemplified when both marker loci are fixed. In this case, while the expectation method is powerless to detect the QTL, the distribution method can still detect the presence (but not the position) of the QTL 59% of the time (when using trait values) or 14% of the time (when using trait differences). We also note a confounding between estimates of the QTL, polygenic, and error variance. The degree of confounding is small when the vector of trait values is used but can be substantial when the expectation method and trait differences are used. We discuss this in light of the general ability of the random model to partition these components.