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The results showed that setting CM data as a binary (model I) or categorical trait (model II) affected the genetic correlation between milk yields (MY) and mastitis occurrence.
Predicted probabilities for CM occurrence scored as a binary or categorical trait were compared to find an appropriate data set for QTL detection.
Previous studies have shown that treating a categorical trait as binary leads to a loss of power [3], [4].
In this paper, we develop a Bayesian statistical framework for linkage analysis of a categorical trait with a user-specified penetrance function of arbitrary form.
Use of the most widely applied software for linkage analysis such as Superlink [5], Merlin [6], Genehunter [7], and LOKI [8] that do not employ categorical trait models is therefore not the most appropriate strategy for analyzing categorical diseases.
In the DSM-IV framework, ADHD is viewed as a categorical trait.
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Our first step in doing so was to differentiate the relevant gene module(s) according to different categorical traits (the different stress conditions, in our case) by the analysis of variance, something which might not have been accomplished by an analysis of correlation.
We implement this framework in the software LOCate (Linkage for Ordinal and Categorical traits).
We believe our simulations establish LOCate as a valuable complementary approach for linkage analysis of categorical traits, particularly nominal traits.
LOCate's accuracy and applicability to both ordinal and nominal traits will prove useful to researchers interested in mapping loci for categorical traits.
We also examined the effect of treating categorical traits as continuous by analyzing simulated pedigrees with Merlin [6] and SOLAR [30].
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