analysis has the advantage of providing correlated data structures that are drawn from inherent comorbidity and/or underlying shared genetic liability, and provides a way to reduce and mine data. The membership coefficients can be considered more homogeneous quantitative traits for linkage analysis. The fuzzy cluster analysis was implemented using an R clustering algorithm known as the “fanny” algorithm [Kaufman and Rousseeuw, 1990]. The result of fuzzy clustering by the fanny algorithm can be summarized by a silhouette value for each subject [Rousseeuw, 1987]. The silhouette value (“Ŝ”, which ranges from −1 to 1) for each subject is a measure of how similar that subject is to subjects in the same cluster compared to subjects in other clusters. Subjects with a large value of “Ŝ” (almost 1) are very well clustered, a small “Ŝ” (around 0) means that the subject lies between two clusters, and subjects with a negative “Ŝ” are probably placed in the wrong cluster. Our study of anxiety disorders was the first to successfully implement fuzzy clustering in a genome scan [Kaabi et al., 2006].
